Software Engineer, Data, AI & UX

📍 Job Overview

Job Title: Software Engineer, Data, AI & UX

Company: Engelhart

Location: Stamford, Connecticut

Job Type: Full-Time

Category: Data Engineering / Backend Software Engineering

Date Posted: 2026-08-27

Experience Level: Mid-Senior Level (Estimated 5-10 years)

Remote Status: On-site

🚀 Role Summary

• This role is a hybrid Backend Software Engineering and Data Engineering position, focusing on building and maintaining scalable services, infrastructure, and data platforms.

• Responsibilities include designing and developing robust backend services and APIs in Python, crucial for data ingestion, processing, and distribution within the organization.

• Key aspects involve constructing and optimizing reliable data ingestion pipelines for diverse data sources, and developing reusable frameworks to enhance developer productivity and data onboarding.

• The position requires hands-on experience with cloud platforms, particularly AWS services, for managing and optimizing data infrastructure, ensuring cost-efficiency and high performance.

📝 Enhancement Note: The job title "Software Engineer, Data, AI & UX" suggests a broad scope, but the description heavily emphasizes backend and data engineering. The "UX" component appears to be minimal, likely related to building internal tools or interfaces for data consumers rather than external-facing user experience design. The role is characterized as primarily focused on engineering tasks rather than extensive stakeholder management, indicating a hands-on technical contributor position. The estimated experience level of 5-10 years is inferred from the depth of responsibilities and the need for production-level system development.

📈 Primary Responsibilities

• Design, develop, and maintain scalable backend services and APIs using Python to facilitate efficient data ingestion, processing, and distribution across the organization.

• Build and maintain robust, reliable data ingestion pipelines, capable of handling structured and unstructured data from a wide array of internal and external sources.

• Develop reusable frameworks and tooling to streamline the onboarding of new data sources and significantly improve developer productivity.

• Architect, design, and optimize scalable data platforms, including databases, data lakes, metadata catalogues, and various storage solutions.

• Drive continuous improvements in the reliability, scalability, observability, and cost-efficiency of the company's data infrastructure.

• Integrate data across disparate systems, focusing on reducing duplication and ensuring the highest standards of data quality.

• Develop and maintain orchestration workflows and automated data processing pipelines to ensure seamless data operations.

• Build internal libraries and services that empower other engineering teams to access and consume data efficiently and effectively.

• Proactively monitor, troubleshoot, and optimize production systems and data pipelines to maintain optimal performance and availability.

• Actively contribute to architecture discussions and critical technical decisions pertaining to the data platform's evolution and strategy.

• Foster close collaboration with software engineers, data engineers, infrastructure specialists, and data consumers to deliver high-impact, business-aligned solutions.

• Continuously identify and implement opportunities for system simplification, enhanced automation, and reduction of operational overhead.

📝 Enhancement Note: The responsibilities highlight a strong focus on building foundational data infrastructure and backend services, indicative of a mid-to-senior level engineer. The emphasis on "reusable frameworks," "optimizing scalable data platforms," and "contributing to architecture discussions" suggests a need for an engineer who can not only execute but also influence technical direction and best practices within the data engineering domain. The inclusion of "AI & UX" in the title, while not heavily detailed in responsibilities, hints at potential future involvement with AI/ML initiatives or internal tooling that could benefit from user-centric design principles.

🎓 Skills & Qualifications

Education: While no specific degree is mandated, a strong academic foundation in Computer Science, Engineering, or a related quantitative field is implicitly expected for a role of this technical depth.

Experience: Proven professional experience in backend software engineering and data engineering, with a track record of building and deploying production-ready systems.

Required Skills:

• Proficient in backend software engineering fundamentals, with extensive professional experience developing production backend systems and APIs using Python.

• Demonstrated full-stack development experience, including building maintainable, user-facing applications and interfaces with Node.js and React, leveraging React-Redux for state management.

• Expertise in designing, developing, and maintaining RESTful APIs and scalable backend services.

• Solid experience in building and maintaining data pipelines, including proficiency with infrastructure automation practices.

• Strong SQL skills, with practical experience working with both relational and analytical databases.

• Hands-on experience with core AWS services such as ECS, SQS, RDS, Athena, S3, Lambda, and CloudWatch, or equivalent cloud technologies.

• Experience working with containerized applications (e.g., Docker) and modern software delivery practices (e.g., CI/CD).

• Familiarity with messaging and asynchronous processing patterns (e.g., SQS, RabbitMQ, Kafka). Preferred Skills:

• Full lifecycle, server-side and client-side, experience with Websockets.

• Experience leveraging AI tools and agentic AI solutions to accelerate software development and business workflows, including familiarity with LLMs, MCPs, tools, skills, knowledge bases, vector databases, AWS Bedrock, LangChain, and PostgreSQL.

• Experience with workflow orchestration tools like Airflow.

• Experience with streaming platforms such as Kafka.

• Experience with Infrastructure as Code (IaC) (e.g., Terraform, CloudFormation) and cloud-native architectures.

• Familiarity with distributed systems and high-throughput data processing concepts.

• Experience with observability, monitoring, and performance tuning of complex systems.

📝 Enhancement Note: The required skills are comprehensive, covering backend development, data engineering, and cloud infrastructure. The emphasis on Python, Node.js, and React indicates a need for a versatile engineer. The inclusion of AWS services is critical, and candidates should be prepared to discuss specific use cases and challenges encountered. The preferred skills, particularly those related to AI and agentic solutions, suggest Engelhart is investing in cutting-edge technologies, and candidates with this experience will have a significant advantage.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase projects demonstrating robust backend service development and API design, with clear explanations of architecture and scalability considerations.

• Include examples of data pipelines you have built or significantly improved, highlighting efficiency gains, data quality improvements, and handling of diverse data sources.

• Present case studies of data infrastructure you have designed or managed, detailing the technologies used, challenges overcome, and performance optimizations.

• Demonstrate experience with cloud services (ideally AWS) through projects that highlight infrastructure automation, cost optimization, and reliability enhancements.

• Provide examples of reusable frameworks or tooling developed that solved common engineering problems or improved team productivity. Process Documentation:

• Detail your approach to designing and implementing data ingestion processes, including error handling, monitoring, and data validation strategies.

• Explain your methodology for optimizing existing data pipelines for performance, cost, and reliability, citing specific metrics and outcomes.

• Illustrate your understanding of workflow orchestration and automation for data processing, potentially using examples from Airflow or similar tools.

• Document your experience with integrating disparate systems and ensuring data consistency and quality across a technology stack.

📝 Enhancement Note: For a role that bridges backend engineering and data engineering, a portfolio should clearly articulate the candidate's ability to build robust, scalable systems and manage complex data flows. Emphasis should be placed on quantifiable improvements and technical depth in areas like API design, data pipeline architecture, cloud infrastructure management, and potentially AI/ML integrations. Candidates should be prepared to walk through their projects, explaining the "why" behind their technical decisions and the impact on business objectives.

💵 Compensation & Benefits

Salary Range: Based on the location (Stamford, CT), experience level (Mid-Senior, estimated 5-10 years), and the technical demands of this role (Backend Engineering, Data Engineering, AWS, Python, Full-Stack), a competitive salary range is estimated between $130,000 - $180,000 per year. This estimate is derived from industry benchmarks for similar roles in the Northeast US tech market, considering the specialized skills in data infrastructure and AI-adjacent technologies.

Benefits:

• Competitive Compensation: Base salary commensurate with experience and market rates.

• Discretionary Bonus Plan: Potential for annual bonuses based on company and individual performance.

• Generous Paid Time Off: 20 days of annual holiday entitlement, in addition to US public holidays (NYSE).

• Comprehensive Health Coverage: Robust benefits package including Medical, Dental, and Vision insurance.

• Life Insurance: Company-provided life insurance policy.

• Retirement Savings: 401(k) plan with a company match, supporting long-term financial planning.

• Supplemental Benefits: Additional benefits partially subsidized by the Company.

• Training & Development: Eligibility for external and internal training programs to foster continuous learning and skill advancement, aligned with the Training & Development Policy.

Working Hours: Full-time role, typically expected to align with a standard 40-hour work week. Given the global nature of trading and the need for system reliability, occasional flexibility may be required outside of standard hours for critical issues or deployments.

📝 Enhancement Note: The salary range is an estimate based on publicly available data for Software Engineers and Data Engineers with 5-10 years of experience in the Stamford, CT area, and considering the competitive nature of specialized tech roles in finance/trading firms. The benefits package is comprehensive, with a notable offering of 20 days of annual holiday plus public holidays, which is generous for a US-based role. The mention of a discretionary bonus plan is standard for performance-driven environments.

🎯 Team & Company Context

🏢 Company Culture

Industry: Commodities Trading and Energy Trading, with a recent expansion into renewable power risk management and optimization services through the acquisition of Trailstone. This sector demands high performance, agility, and robust technological infrastructure.

Company Size: The acquisition of Trailstone likely means Engelhart has grown significantly, potentially moving into the 200-500+ employee range or larger, indicating a substantial operation with complex data needs.

Founded: Engelhart was founded in 2013, with Trailstone acquired in 2024, signifying a company with established operational expertise and a forward-looking strategy for growth and technological integration.

Team Structure:

• The Data team is responsible for core data infrastructure, ingestion, processing, storage, and distribution.

• This role is within the Data team, focusing on backend and data engineering aspects, with close collaboration expected with other engineering teams (software, infrastructure) and data consumers.

• The structure is likely project-oriented and cross-functional, emphasizing collaboration to deliver business value. Methodology:

• Data-driven decision-making is a core tenet, supported by advanced fundamental analysis, quantitative research, and weather research capabilities.

• Emphasis on strong risk management practices, powerful technology, and operational excellence.

• The acquisition of Trailstone suggests an integration of advanced analytics and proprietary technology into their operational framework.

Company Website: https://www.engelhart.com/

📝 Enhancement Note: Engelhart operates in a fast-paced, high-stakes industry where data accuracy, speed, and reliability are paramount. The company culture values Performance, Agility, Collaboration, and Entrepreneurship, suggesting an environment where initiative, quick adaptation, and teamwork are highly prized. The integration of Trailstone indicates a strategic move towards leveraging technology and data analytics for renewable energy optimization, positioning the company at the forefront of energy transition technologies.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Mid-Senior level Software Engineer with a strong specialization in Data Engineering. It requires a solid foundation in backend development and the ability to design, build, and maintain complex data systems independently. The scope includes contributing to architectural discussions, indicating a move towards technical leadership or senior individual contributor roles.

Reporting Structure: The role reports into the Data team, likely under a Data Engineering Manager or Head of Data. Collaboration will extend across various engineering disciplines and potentially business units that consume data.

Operations Impact: The Data team's output directly impacts the company's ability to make informed trading decisions, manage risk effectively, develop innovative products (like renewable energy optimization services), and ensure operational efficiency. This role is crucial for providing the reliable data foundation that underpins these critical business functions.

Growth Opportunities:

• Technical Specialization: Deepen expertise in data engineering, cloud architecture (AWS), AI/ML infrastructure, and potentially distributed systems and real-time data processing.

• Leadership Development: Potential to move into senior individual contributor roles (e.g., Principal Engineer) or transition into management roles (e.g., Data Engineering Lead) as the team and company grow.

• Cross-Functional Exposure: Gain experience working with diverse teams across trading, quantitative research, and business development, understanding their data needs and contributing to strategic initiatives.

• Emerging Technologies: Opportunity to work with cutting-edge AI tools and agentic AI solutions, contributing to the company's innovation in software development and business workflows.

📝 Enhancement Note: The growth trajectory for this role is strong, given Engelhart's focus on technology and data in the dynamic energy and commodities markets. The integration of AI capabilities suggests significant opportunities for engineers to expand their skill sets into advanced areas. The company's emphasis on entrepreneurship and agility means proactive individuals can carve out significant impact and career progression paths.

🌐 Work Environment

Office Type: On-site work is specified, indicating a traditional office environment. This is common in financial trading firms where collaboration, security, and immediate access to infrastructure are critical.

Office Location(s): Stamford, Connecticut. This location is a significant financial hub, offering good connectivity and access to talent.

Workspace Context:

• The workspace is expected to be collaborative, fostering interaction with other engineers, data scientists, and potentially traders or analysts.

• Access to modern development tools, high-performance computing resources, and robust network infrastructure will be standard.

• The environment likely encourages a blend of focused individual work and team-based problem-solving sessions.

Work Schedule: Standard full-time hours (approx. 40 hours/week) are expected. However, the nature of commodities and energy trading means that systems must be operational 24/7. While the core role is focused on development, engineers may be involved in on-call rotations or need to respond to critical incidents outside of standard hours to ensure system uptime and data integrity.

📝 Enhancement Note: The on-site requirement in Stamford suggests a preference for in-person collaboration, which is beneficial for complex technical discussions and team cohesion in a high-paced environment like financial trading. The potential for on-call responsibilities underscores the critical nature of the data infrastructure for Engelhart's business operations.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review applications and conduct an initial phone screen to assess basic qualifications, experience, and cultural fit.

• Technical Assessment: Expect one or more technical interviews, likely including:

  • Coding Challenges: Practical coding exercises focusing on Python, data structures, algorithms, and SQL. These might be live coding sessions or take-home assignments.
  • System Design: Discussions around designing scalable backend services, data pipelines, or data infrastructure, emphasizing trade-offs, scalability, and reliability.
  • Data Engineering Concepts: Questions on data modeling, ETL/ELT processes, database technologies, and cloud data services.

• Team/Manager Interviews: Meetings with potential team members and the hiring manager to discuss detailed responsibilities, team dynamics, career growth, and the candidate's approach to problem-solving and collaboration.

• Final Interview: Potentially a discussion with a senior leader or executive to assess strategic thinking and overall fit with the company's vision and values.

Portfolio Review Tips:

• Curate Selectively: Choose 2-3 key projects that best demonstrate your backend engineering and data engineering skills, aligning with the job requirements (Python, AWS, data pipelines, APIs).

• Focus on Impact: For each project, clearly articulate the problem you solved, your specific contributions, the technologies used, and the measurable outcomes or business impact (e.g., performance improvements, cost savings, new capabilities enabled). Use metrics wherever possible.

• Showcase Architecture: Be prepared to walk through the architecture of your projects, explaining design decisions, trade-offs, and scalability considerations. Diagrams can be very helpful.

• Highlight Collaboration: If applicable, describe how you collaborated with other engineers, product managers, or stakeholders.

• Prepare for Deep Dives: Anticipate detailed questions about your code, design choices, and problem-solving approach.

Challenge Preparation:

• Practice Python & SQL: Brush up on core Python programming, data structures, algorithms, and advanced SQL querying techniques.

• Review System Design: Study common patterns for building scalable backend services, APIs, and data pipelines. Understand concepts like microservices, asynchronous processing, and database optimization.

• AWS Services: Familiarize yourself with the AWS services mentioned (ECS, SQS, RDS, S3, Lambda, CloudWatch) and common use cases for data engineering.

• AI/ML Concepts (Optional but Recommended): If you have experience with AI tools, be ready to discuss them, especially LLMs, vector databases, and frameworks like LangChain.

📝 Enhancement Note: The interview process is likely rigorous, typical for tech roles in financial services. Candidates should prepare for both theoretical and practical assessments. A strong portfolio that showcases tangible results and technical depth will be crucial for differentiation. The emphasis on AI tools in the "highly desirable" section suggests that candidates who can speak to these technologies will be highly competitive.

🛠 Tools & Technology Stack

Primary Tools:

• Backend Development: Python (core language), potentially frameworks like Flask or FastAPI for API development.

• Frontend Development (for internal tools/interfaces): Node.js, React, React-Redux.

• Databases: Relational databases (e.g., PostgreSQL, MySQL) and Analytical databases (e.g., Redshift, Snowflake, or similar column-store databases). Strong SQL proficiency required.

• Cloud Platform: Amazon Web Services (AWS) services such as ECS (container orchestration), SQS (message queueing), RDS (managed relational databases), Athena (serverless query service), S3 (object storage), Lambda (serverless compute), CloudWatch (monitoring).

Analytics & Reporting:

• Tools for data processing and analysis, potentially including services like AWS Athena, and internal BI tools or custom dashboards.

• Emphasis on building systems that enable efficient data access for analytics and reporting. CRM & Automation:

• While not explicitly mentioned, internal systems for managing data sources, workflows, and operational tasks are implied.

• Workflow Orchestration: Experience with tools like Airflow is highly desirable.

• Messaging/Streaming: Familiarity with Kafka or similar streaming platforms is a plus.

• Containerization: Docker for containerized applications.

📝 Enhancement Note: The technology stack is modern and cloud-centric, with a strong emphasis on AWS. Proficiency in Python for backend and data processing, along with SQL for data querying, is non-negotiable. The inclusion of Node.js and React suggests the team might build internal dashboards or tools that require full-stack capabilities. The "highly desirable" list points towards an interest in adopting advanced AI/ML tooling and robust orchestration/streaming technologies.

👥 Team Culture & Values

Operations Values:

• Performance: A strong emphasis on delivering high-quality, reliable systems and achieving measurable results in a demanding trading environment.

• Agility: The ability to adapt quickly to changing market conditions, technological advancements, and business priorities. This translates to iterative development and flexible problem-solving.

• Collaboration: Working effectively across different engineering disciplines (backend, data, infrastructure) and with data consumers to achieve shared goals. Open communication and knowledge sharing are key.

• Entrepreneurship: Taking initiative, owning problems, and proactively seeking opportunities to improve systems, processes, and business outcomes. This encourages innovation and a sense of ownership.

Collaboration Style:

• Highly cross-functional, with engineers expected to work closely with other technical teams and potentially business stakeholders to understand data needs and deliver solutions.

• A culture that likely values constructive feedback, code reviews, and shared problem-solving sessions to ensure technical excellence and continuous improvement.

• Emphasis on clear communication, documentation, and knowledge sharing to build a robust and maintainable data platform.

📝 Enhancement Note: The company values directly translate into expectations for how team members operate. Engineers are expected to be high-achievers who can adapt quickly, work seamlessly with others, and take ownership of their contributions, driving innovation and efficiency within the data domain.

⚡ Challenges & Growth Opportunities

Challenges:

• Data Volume & Velocity: Managing and processing large volumes of diverse data reliably and cost-effectively in a high-frequency trading environment.

• System Complexity: Building and maintaining intricate data pipelines and backend services that integrate various internal and external systems.

• Keeping Pace with Technology: Staying current with rapidly evolving cloud technologies, AI advancements, and best practices in data engineering and software development.

• Balancing Technical Debt: Continuously improving system reliability and scalability while managing existing infrastructure and meeting business demands.

Learning & Development Opportunities:

• Skill Deepening: Opportunities to become an expert in specific AWS services, distributed systems, real-time data processing, and advanced data modeling techniques.

• AI Integration: Hands-on experience with cutting-edge AI tools, LLMs, and agentic AI solutions, contributing to innovative applications.

• Industry Exposure: Gaining deep insights into the commodities and energy trading markets, understanding how data infrastructure directly supports business success.

• Mentorship & Leadership: Potential for mentorship from senior engineers and opportunities to lead technical initiatives or mentor junior team members as career progresses.

📝 Enhancement Note: The challenges are typical of high-growth tech companies in specialized industries. The opportunities for learning and development are significant, especially with the company's investment in AI and its position in the evolving energy market. Engineers who embrace these challenges will find ample room for professional growth.

💡 Interview Preparation

Strategy Questions:

• Data Platform Strategy: "How would you approach designing a scalable data ingestion pipeline for real-time market data, considering latency, reliability, and cost?" (Focus on architecture, AWS services, trade-offs).

• Backend Service Design: "Describe a complex backend service you built. What were the key challenges, and how did you ensure its scalability and maintainability?" (Highlight Python skills, API design, error handling).

• Problem Solving & Optimization: "Imagine a data pipeline is experiencing significant latency. What steps would you take to diagnose and resolve the issue?" (Demonstrate systematic debugging, monitoring tools, performance tuning).

Company & Culture Questions:

• Motivation: "Why are you interested in Engelhart and this specific role, given our industry?" (Research Engelhart's business, values, and recent acquisitions like Trailstone).

• Collaboration Style: "Describe a time you had to collaborate with engineers from different disciplines (e.g., infrastructure, frontend) to deliver a project. What was your approach?" (Align with Engelhart's collaboration value).

• Entrepreneurship: "Tell me about a time you identified an opportunity to improve a process or system proactively, even if it wasn't explicitly assigned to you." (Showcase initiative and alignment with the entrepreneurship value).

Portfolio Presentation Strategy:

• Quantify Impact: For each project, clearly state the business problem, your solution, and the measurable results (e.g., "Reduced data processing time by 30%," "Enabled new reporting capability leading to X% revenue uplift").

• Technical Depth: Be prepared to discuss architectural diagrams, specific code implementations, challenges overcome, and the rationale behind your technology choices.

• Storytelling: Frame your projects as narratives – the challenge, your approach, the solution, and the outcome. This makes your contributions memorable.

• Tooling Context: Explicitly mention the tools and technologies you used for each project, especially those relevant to the job description (Python, AWS, SQL, React, etc.).

📝 Enhancement Note: Interview preparation should focus on demonstrating not just technical proficiency but also an understanding of the business context (trading, energy markets) and alignment with Engelhart's core values. Candidates should be ready to discuss their past projects in detail, emphasizing impact and technical decision-making.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided greenhouse.io link.

• Tailor Your Resume: Highlight experience with Python, backend development, data pipelines, AWS services, SQL, and any full-stack experience using Node.js/React. Quantify achievements with metrics where possible.

• Curate Your Portfolio: Select 2-3 impactful projects that showcase your backend engineering and data engineering skills, focusing on scalability, reliability, and business impact. Prepare to present these projects with clear explanations of architecture and outcomes.

• Prepare for Technical Interviews: Practice coding challenges in Python and SQL. Review system design principles for backend services and data pipelines, and refresh your knowledge of core AWS services.

• Research Engelhart: Understand the company's business in commodities and energy trading, their recent acquisition of Trailstone, and their stated values (Performance, Agility, Collaboration, Entrepreneurship) to articulate your fit during interviews.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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