Specialist, Product Strategy

📍 Job Overview

Job Title: Data Engineer - Business Transformation

Company: Fiserv

Location: Noida, Uttar Pradesh, India

Job Type: Full time

Category: Data Engineering / Business Transformation Operations

Date Posted: 2026-08-13

Experience Level: 2-5 Years

Remote Status: On-site

🚀 Role Summary

• Design, develop, and maintain scalable data pipelines for business transformation initiatives, ensuring data integrity and reliability.

• Collaborate with business stakeholders, technology teams, and analytics professionals to define data requirements, map source systems, and resolve data gaps.

• Build and manage data models, reusable datasets, and governed analytics layers to foster consistency and trust in reporting and decision-making.

• Implement and monitor ETL/ELT processes using SQL and Python, focusing on automation, data quality, and timely refresh cycles.

• Create and maintain executive dashboards, KPI scorecards, and key data assets to support strategic transformation programs.

📝 Enhancement Note: This role is positioned within a business transformation context, implying a focus on data supporting strategic projects and change management. The emphasis on "trusted reporting" and "decision support" highlights the critical nature of accurate and accessible data for leadership. The operations aspect is centered around building and maintaining the data infrastructure that enables these transformation goals.

📈 Primary Responsibilities

• Identify and document critical data sources, owners, key fields, dependencies, and existing data gaps for various transformation initiatives.

• Partner closely with business, technology, finance, and operations teams to meticulously document data flows, lineage, metric definitions, and specific reporting requirements.

• Establish and enforce common data models, reusable datasets, and governed analytics layers to ensure consistency and accuracy across all transformation-related analytics.

• Develop, test, and maintain robust, automated ETL/ELT processes leveraging SQL, Python, and modern data platform tools to ingest and transform data.

• Monitor the reliability, data quality, and refresh timelines of data pipelines, proactively addressing issues to ensure the delivery of trusted reporting outputs.

• Construct and maintain comprehensive dashboards, recurring data views, KPI scorecards, and other essential data assets specifically for transformation programs.

• Standardize analytical logic, metric definitions, data dictionaries, and technical documentation across all priority transformation initiatives to ensure a unified understanding.

• Support and actively participate in data governance practices, including access control, data quality assurance, metadata management, and ensuring usage consistency.

📝 Enhancement Note: The responsibilities clearly indicate a hands-on Data Engineering role with a strong emphasis on operationalizing data for strategic business purposes. The focus on "documentation," "standardization," and "governance" points to the need for meticulous process adherence and a systematic approach to data management, crucial for transformation projects.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related quantitative field.

Experience: 2-4 years of progressive experience in data engineering, data platforms, business intelligence, or related data functions, with a demonstrated track record of building and managing data solutions.

Required Skills:

• Advanced proficiency in SQL for complex querying, data manipulation, and performance optimization.

• Strong programming skills in Python for data pipeline development, automation, and data transformation tasks.

• Hands-on experience with ETL/ELT tools and methodologies, including designing, building, and maintaining data flows.

• Solid understanding of data modelling principles (e.g., dimensional, relational) and experience in creating scalable data structures.

• Proven ability to develop and maintain business intelligence dashboards and reporting solutions.

• Experience working with cloud data platforms (e.g., AWS, Azure, GCP) and their associated data services.

• Practical understanding of data governance concepts, including data quality, metadata management, and data lineage.

• Strong analytical and problem-solving skills, with a keen attention to detail and a commitment to data accuracy.

• Excellent communication and collaboration skills to effectively translate business requirements into technical solutions and work with cross-functional teams. Preferred Skills:

• Experience in merchant data infrastructure, billing systems, payment processing, fintech, or financial services environments.

• Exposure to AI/ML use cases, semantic layers, data catalogs, and modern data architecture modernization initiatives.

• Proficiency with BI and analytics tools such as Power BI, Tableau, SQL, Alteryx, or R.

• Prior experience supporting executive-level dashboards, KPI standardization, and enterprise-wide reporting programs.

• Familiarity with agile development methodologies for data projects.

📝 Enhancement Note: The experience requirements suggest a mid-level Data Engineer capable of independent work and contributing to complex projects. The preferred skills highlight a desire for domain expertise within financial services and exposure to emerging data technologies, indicating potential future growth areas for the role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of complex SQL queries and Python scripts used for data extraction, transformation, and loading (ETL/ELT).

• Examples of data models designed for analytical purposes, showcasing understanding of relational and dimensional modeling.

• Case studies of automated data pipelines built, highlighting efficiency gains and reliability improvements.

• Visualizations of dashboards or reports created using BI tools, illustrating data storytelling and key metric presentation.

• Documentation samples demonstrating data lineage, metric definitions, and data dictionary entries. Process Documentation:

• Showcase of workflow design and optimization for data pipelines, detailing stages from source to consumption.

• Evidence of implementing and managing data quality checks and validation processes within automated workflows.

• Examples of creating reusable data assets, such as views, stored procedures, or curated datasets, for broader analytical use.

• Documentation of data governance practices applied, such as metadata management or access control protocols.

📝 Enhancement Note: Given the role's focus on "business transformation" and "trusted reporting," candidates will be expected to showcase their ability to not only build data solutions but also to document and govern them effectively. The portfolio should emphasize the operational aspects of data engineering, focusing on reliability, scalability, and stakeholder enablement.

💵 Compensation & Benefits

Salary Range: For a Data Engineer with 2-4 years of experience in Noida, India, the estimated annual salary range is ₹700,000 to ₹1,200,000. This estimate is based on industry benchmarks for similar roles in the region, considering the required technical skills (SQL, Python, ETL/ELT) and the emphasis on business transformation support.

Benefits:

• Comprehensive health insurance coverage (medical, dental, vision).

• Retirement savings plan with company matching contributions.

• Paid time off, including vacation days, sick leave, and public holidays.

• Opportunities for professional development, training, and certifications in data engineering and related fields.

• Employee assistance program offering confidential counseling and support services.

• Potential for performance-based bonuses and annual increments.

Working Hours: Standard full-time position, typically 40 hours per week, with potential for occasional overtime to meet critical project deadlines or address urgent data issues. The role is on-site, requiring consistent presence at the Noida office.

📝 Enhancement Note: The salary range is an estimation based on general market data for Data Engineers in Noida, India, with 2-4 years of experience, and the specific technical skills mentioned. Fiserv's actual compensation may vary based on individual qualifications, performance, and internal compensation structures. Benefits are typical for a large, established technology company.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Technology (Fintech) and Payments. Fiserv is a global leader in providing technology solutions for financial institutions, merchants, and corporations, enabling secure and efficient money movement and information exchange.

Company Size: Fiserv is a large enterprise, employing over 40,000 associates globally. This size indicates a robust organizational structure, established processes, and significant resources available for technology and innovation. For operations professionals, this means opportunities for specialization, but also the need to navigate larger corporate structures and processes.

Founded: Fiserv was founded in 1984, bringing decades of experience and a deep understanding of the financial services and payments industry. This long history suggests stability, a mature operational framework, and a well-established market presence.

Team Structure:

• The Data Engineering team likely operates within a broader Data & Analytics or Technology division, supporting various business units and strategic initiatives.

• The team may be structured around specialized functions (e.g., data warehousing, data pipelines, BI development) or project-based teams, especially for transformation initiatives.

• Collaboration is expected with business analysts, data scientists, BI developers, and stakeholders from finance, operations, and product management. Methodology:

• Data-driven decision-making is paramount, with a strong emphasis on building reliable data foundations for reporting and analytics.

• Agile methodologies are likely employed for project execution, facilitating iterative development and continuous improvement of data pipelines and solutions.

• A focus on automation and efficiency in data processing and reporting to drive business transformation goals.

• Emphasis on data quality, governance, and security, especially given the sensitive nature of financial data.

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

📝 Enhancement Note: Fiserv's position as a global Fintech leader implies a dynamic environment with a strong focus on innovation and operational excellence. The "Business Transformation" aspect of the role suggests involvement in high-priority, strategic projects that aim to improve efficiency, enhance customer experience, or introduce new capabilities within the company.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as a "Specialist" or mid-level Data Engineer. It requires a solid foundation in data engineering principles and tools, with the ability to independently execute tasks and contribute to the design and implementation of data solutions. It's a crucial step for professionals looking to deepen their expertise in data infrastructure and its application in business strategy.

Reporting Structure: The Data Engineer will likely report to a Data Engineering Manager or Lead, who oversees the team's technical execution and project delivery. They will work closely with Project Managers or Transformation Leads for specific initiatives.

Operations Impact: This role has a direct impact on Fiserv's ability to execute its business transformation strategies. By providing accurate, accessible, and timely data, the Data Engineer enables leadership to make informed decisions, monitor progress, identify risks, and measure the success of key initiatives. The quality of data infrastructure directly influences the effectiveness of reporting and analytics, which in turn drives strategic outcomes.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific cloud data platforms (AWS, Azure, GCP), advanced data modeling techniques, or big data technologies.

• Domain Expertise: Develop specialized knowledge in financial services, payments, or merchant data infrastructure, becoming a go-to expert for these areas.

• Leadership Potential: Progress to a Senior Data Engineer role, leading complex projects, mentoring junior engineers, or moving into a Data Engineering Lead or Management position.

• Cross-Functional Roles: Transition into roles focused on Data Architecture, Analytics Engineering, or Data Governance, leveraging a strong understanding of data systems and business needs.

📝 Enhancement Note: The "Specialist" title suggests a role focused on execution and technical proficiency, with clear pathways for advancement into more senior technical or leadership roles within Fiserv's growing data organization. The emphasis on business transformation provides a unique opportunity to impact strategic decisions.

🌐 Work Environment

Office Type: This is an on-site role, indicating a traditional office-based work environment. Fiserv likely provides modern office facilities designed to support collaborative work and individual focus.

Office Location(s): Noida, Uttar Pradesh, India. This location is a significant business and technology hub in India, offering access to a skilled talent pool and a well-developed infrastructure.

Workspace Context:

• Collaborative Environment: The office setting will facilitate face-to-face interaction, spontaneous discussions, and team-based problem-solving, which are crucial for complex data projects and transformation initiatives.

• Operations Tools & Technology: Access to Fiserv's standard IT infrastructure, including high-performance workstations, secure network access, and potentially specialized software licenses for data engineering and BI tools.

• Team Interaction: Opportunities for regular interaction with immediate team members, cross-functional colleagues, and potentially senior leadership during project reviews or strategy sessions.

Work Schedule: The standard work schedule is expected to be Monday to Friday, aligning with typical business hours in India (Asia/Kolkata timezone). While a 40-hour work week is standard, there might be flexibility for employees to manage their schedules, provided project deadlines and team collaboration needs are met. Occasional work outside standard hours may be required for critical deployments or issue resolution.

📝 Enhancement Note: The on-site requirement suggests an environment where direct collaboration and team cohesion are valued. For operations roles, this can foster a strong sense of shared responsibility and facilitate efficient problem-solving through immediate access to colleagues and resources.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or HR representative will conduct an initial call to assess basic qualifications, interest in the role, and cultural fit.

• Technical Assessment: Expect a technical interview or coding challenge focusing on SQL, Python, data modeling, and ETL/ELT concepts. This may involve live coding or a take-home assignment.

• Hiring Manager Interview: A discussion with the hiring manager to delve deeper into your experience, problem-solving approach, and alignment with team goals and Fiserv's business transformation objectives.

• Team/Peer Interviews: Meetings with potential colleagues to evaluate technical collaboration, communication style, and how you would integrate into the existing team dynamic.

• Final Interview: Potentially with a senior leader or director to discuss strategic alignment, career aspirations, and overall fit within Fiserv.

Portfolio Review Tips:

• Showcase Process & Impact: For each project, clearly articulate the business problem, your role, the technical solution implemented (ETL/ELT, data modeling), the tools used, and most importantly, the measurable impact or outcome achieved (e.g., improved reporting efficiency, data accuracy, enabled decision-making).

• Highlight Data Governance: Include examples demonstrating your understanding and application of data quality, metadata management, and data lineage principles. This is critical for "trusted reporting."

• Demonstrate Automation: Present case studies of automated data pipelines, emphasizing how you reduced manual effort, improved reliability, and ensured timely data delivery.

• Tailor to Transformation: If possible, select portfolio pieces that align with business transformation, strategic initiatives, or process improvement themes, demonstrating your ability to support such projects.

• Clear Documentation: Ensure any code snippets or diagrams are well-commented and easy to understand. For dashboards, focus on clarity of metrics and user experience.

Challenge Preparation:

• SQL & Python Proficiency: Be ready to write SQL queries for various scenarios (joins, aggregations, window functions) and Python code for data manipulation and scripting. Practice common data engineering tasks.

• Data Modeling Scenarios: Prepare to discuss how you would model data for different analytical use cases, explaining the trade-offs between relational and dimensional models.

• ETL/ELT Design: Be prepared to design an ETL/ELT process for a given scenario, considering data sources, transformations, error handling, and scheduling.

• Problem-Solving: Practice breaking down complex data problems, identifying root causes, and proposing scalable and efficient solutions.

📝 Enhancement Note: The interview process is designed to assess both technical depth and the ability to apply those skills in a business context, particularly within transformation projects. A well-curated portfolio that clearly demonstrates impact and operational rigor will be a significant advantage.

🛠 Tools & Technology Stack

Primary Tools:

• SQL: Essential for data querying, manipulation, and database interaction. Proficiency in advanced SQL is expected.

• Python: The primary scripting language for building data pipelines, automation, data transformation, and potentially integrating with other systems. Libraries like Pandas, NumPy, and SQLAlchemy are likely used.

• ETL/ELT Tools: Experience with modern ETL/ELT frameworks and tools, whether cloud-native (e.g., AWS Glue, Azure Data Factory) or third-party solutions.

Analytics & Reporting:

• BI Tools: Experience with business intelligence platforms such as Power BI, Tableau, or similar tools for dashboard creation and data visualization.

• Data Warehousing/Lakehouse: Familiarity with cloud-based data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) or data lake architectures.

CRM & Automation:

• Cloud Platforms: Working knowledge of at least one major cloud provider's data services (e.g., AWS S3, EC2, Lambda; Azure Data Lake Storage, Azure Functions).

• Data Governance Tools: Familiarity with tools or concepts related to metadata management, data catalogs, and data lineage tracking.

• Version Control: Proficiency with Git for code management and collaboration.

📝 Enhancement Note: The core stack revolves around SQL and Python for data manipulation and pipeline development, supported by cloud platforms and BI tools. The emphasis on "modern data platform tools" suggests a preference for cloud-native solutions and best practices in data architecture.

👥 Team Culture & Values

Operations Values:

• Data-Driven Excellence: A commitment to building and maintaining highly accurate, reliable, and accessible data assets that empower informed business decisions and drive transformation.

• Efficiency & Automation: A proactive approach to identifying opportunities for automating data processes, reducing manual effort, and improving the speed and scalability of data solutions.

• Collaboration & Partnership: A strong emphasis on working effectively with business stakeholders, technology teams, and analytics professionals to understand needs and deliver solutions that drive tangible value.

• Continuous Improvement: A mindset of constantly seeking ways to optimize data pipelines, enhance data quality, and refine reporting processes to meet evolving business requirements.

• Integrity & Governance: Upholding high standards for data quality, security, and compliance, ensuring that data is managed responsibly and ethically.

Collaboration Style:

• Cross-Functional Integration: The Data Engineer will be expected to actively collaborate with various departments, acting as a bridge between technical data capabilities and business objectives.

• Agile & Iterative: A willingness to work in an agile manner, incorporating feedback, iterating on solutions, and adapting to changing priorities within transformation projects.

• Knowledge Sharing: A culture that encourages sharing best practices, technical insights, and learnings within the team and with broader analytics communities.

• Problem-Solving Focus: A team environment that tackles challenges collaboratively, leveraging diverse perspectives to find the most effective and scalable solutions.

📝 Enhancement Note: The team culture is likely characterized by a blend of technical rigor, business acumen, and a collaborative spirit, essential for driving successful business transformations through data.

⚡ Challenges & Growth Opportunities

Challenges:

• Data Complexity & Silos: Navigating and integrating data from disparate and potentially legacy source systems within a large enterprise.

• Evolving Business Needs: Adapting data solutions to meet the dynamic requirements of ongoing business transformation initiatives.

• Ensuring Data Quality & Trust: Maintaining high standards for data accuracy and reliability across all automated pipelines and reporting outputs.

• Scalability & Performance: Designing and implementing data solutions that can scale effectively to handle growing data volumes and user demands.

• Balancing Speed and Governance: Finding the right balance between rapid delivery of data solutions and adhering to robust data governance policies.

Learning & Development Opportunities:

• Advanced Data Technologies: Opportunities to gain hands-on experience with cutting-edge data platforms, cloud services, and potentially AI/ML integration.

• Industry Expertise: Deepen knowledge of the Fintech and payments industry, understanding the unique data challenges and opportunities within Fiserv's domain.

• Project Leadership: Potential to lead data sub-projects within larger transformation initiatives, developing project management and stakeholder management skills.

• Certification Programs: Support for obtaining relevant certifications in cloud technologies, data engineering, or specific BI tools.

• Mentorship: Access to senior engineers and architects for guidance and career development advice.

📝 Enhancement Note: This role offers significant opportunities for professional growth by tackling complex data challenges in a dynamic industry and leveraging Fiserv's resources for continuous learning and skill development.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to build a data pipeline from scratch for a new initiative. What were the key challenges, and how did you overcome them?" (Focus on process, problem-solving, and outcome).

• "How do you ensure data quality and reliability in your ETL/ELT processes, especially when dealing with multiple data sources?" (Highlight your data governance and validation techniques).

• "Walk me through how you would approach designing a data model for a new reporting requirement that involves sales, customer, and transaction data." (Demonstrate your data modeling thought process).

• "Imagine a critical dashboard is showing incorrect data. What steps would you take to diagnose and resolve the issue?" (Showcase your troubleshooting methodology).

• "How do you collaborate with non-technical stakeholders to gather data requirements and explain technical concepts?" (Emphasize communication and business acumen). Company & Culture Questions:

• "What interests you about Fiserv and our role in the Fintech industry?" (Research Fiserv's mission, recent news, and its impact).

• "How do you see this Data Engineer role contributing to Fiserv's business transformation efforts?" (Connect your skills to the role's strategic objectives).

• "Describe your experience working in a large, established company versus a startup. What are the pros and cons?" (Showcase adaptability and understanding of corporate environments).

• "How do you stay updated with the latest trends and technologies in data engineering?" (Highlight your commitment to continuous learning). Portfolio Presentation Strategy:

• STAR Method: For each project, structure your explanation using the Situation, Task, Action, and Result (STAR) method. Clearly define the business context, your specific role, the actions you took, and the quantifiable results achieved.

• Focus on Impact: Quantify your achievements whenever possible (e.g., "reduced data processing time by 30%", "enabled faster reporting for X team", "improved data accuracy by Y%").

• Technical Depth & Clarity: Be prepared to dive into technical details of your solutions (e.g., specific SQL functions used, Python libraries, data modeling choices) but also explain them in a way that a non-technical audience can understand.

• Demonstrate Process: Show, don't just tell. Use diagrams, code snippets (if appropriate and anonymized), or screenshots of dashboards to illustrate your work.

• Address Governance: Explicitly mention how you incorporated data quality, security, and governance principles into your projects.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of strong technical skills, a problem-solving mindset, effective communication, and an understanding of how data engineering supports broader business objectives, especially within a transformation context.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided application link on the Fiserv careers portal.

• Tailor Your Resume: Customize your resume to highlight specific experiences and skills mentioned in the job description, particularly SQL, Python, ETL/ELT, data modeling, and BI. Use keywords from the posting to ensure ATS compatibility and clearly articulate achievements related to data engineering and business transformation.

• Prepare Your Portfolio: Gather 2-3 key projects that best demonstrate your capabilities in building robust data pipelines, designing data models, and creating impactful reports/dashboards. Be ready to discuss the business problem, your solution, the technologies used, and the measurable outcomes.

• Research Fiserv: Familiarize yourself with Fiserv's business, its role in the Fintech industry, its products, and its recent news. Understand how data engineering contributes to their strategic goals, especially business transformation.

• Practice Interview Responses: Prepare for technical questions on SQL, Python, and data engineering concepts, as well as behavioral questions using the STAR method. Practice articulating your portfolio projects concisely and impactfully.

⚠️ 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.