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Key Responsibilities and Required Skills for Informatica Developer

💰 $80,000 to $130,000 per year

TechnologyData EngineeringETL

🎯 Role Definition

An Informatica Developer is responsible for designing, building, implementing, and maintaining complex data integration and ETL (Extract, Transform, Load) solutions using the Informatica PowerCenter or Informatica Intelligent Cloud Services (IICS) platform. This role translates business requirements into technical mappings, workflows, sessions and integrations, collaborates with data architects, analysts and DBAs to ensure high‑quality, scalable data pipelines, and supports production environments through optimization, troubleshooting and documentation. The Informatica Developer plays a critical role in enabling data warehousing, analytics, data migration, and ensuring data integrity and performance across systems.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior ETL Developer or Data Integration Developer
  • SQL Developer or Database Developer transitioning into ETL/integration
  • BI Developer or Data Warehouse Support Engineer

Advancement To:

  • Senior Informatica Developer or Data Integration Lead
  • Data Warehouse Architect or Integration Architect
  • Director of Data Engineering, Head of Data Integration or Principal Data Engineer

Lateral Moves:

  • Big Data Engineer specializing in Spark/Hadoop/Snowflake
  • Cloud Data Engineer (e.g., AWS/Azure/Google) with ETL focus
  • Data Governance / Data Quality Specialist

Core Responsibilities

Primary Functions

  1. Design, develop and maintain ETL mappings, workflows, sessions, and tasks in Informatica PowerCenter or IICS to move data from source systems into data warehouse or data lake environments.
  2. Work with business analysts, data architects and stakeholders to gather requirements, analyse source data structures, craft technical specification documents and map business rules into ETL logic.
  3. Optimize and tune ETL jobs and mappings—including transformations, push‑down optimization, partitioning, indexing, buffer memory settings, workflow optimization—to meet performance SLAs.
  4. Develop complex SQL queries, stored procedures, triggers and Oracle/SQL Server routines to support data ingestion, transformation, cleansing and staging operations.
  5. Monitor, schedule, and manage ETL jobs, handle job failures, perform root cause analysis, implement corrective actions and ensure full production uptime and reliability.
  6. Support data migrations including old system to new system ETL, flat‑file ingestion, data cleansing, legacy to modern platform conversion and subsequent documentations.
  7. Maintain technical documentation including mapping specs, data flow diagrams, design documentation, operational run‑books, change logs and version control of ETL code.
  8. Ensure data quality, integrity and governance by implementing data validation, reconciliation, audit trails, error‑handling frameworks and working with Data Quality tools.
  9. Collaborate with DBAs and infrastructure teams to manage and optimise database architecture, performance, partitioning, table space, and scheduling for ETL loads.
  10. Work with cloud or hybrid environments, leveraging Informatica Cloud or other data integration tools with Snowflake, Redshift, BigQuery, ADLS/S3 or Databricks when needed.
  11. Lead unit, integration and system testing of ETL jobs and mappings, validate data load accuracy, support QA and ensure readiness for production deployments.
  12. Participate in agile development methodology (sprint planning, backlog grooming, stand‑ups, retrospectives) and provide estimates and status updates for ETL tasks.
  13. Perform data profiling, source‑target validation, identify anomalies, assist in designing solutions to clean, standardise and transform data for analytics consumption.
  14. Manage and maintain ETL code repositories, branch and version control (Git/SVN), coordinate releases, sandbox refreshes and environment migrations.
  15. Support 24x7 or rotational on‑call duties as required for ETL production loads, incident resolution, hot‑fix implementation and schedule management.
  16. Mentor and coach junior ETL/informatica developers, conduct code reviews, promote best practices and standardise development methodology across the team.
  17. Assist in designing and implementing scalable data architectures and frameworks (e.g., data vault, star/snowflake schemas) that underpin data warehouse initiatives.
  18. Monitor and report on ETL process metrics, job execution performance, data volume trends, resource utilisation, and recommend improvements to maintain efficiency.
  19. Conduct metadata management, lineage tracking, and collaborate with data stewards to ensure data discovery, cataloguing and traceability of data flows.
  20. Evaluate and adopt new Informatica modules or complementary technologies (e.g., Informatica Data Quality, Big Data Management, cloud integration) to enhance data integration capabilities.

Secondary Functions

  • Support ad‑hoc data integration or extraction requests from business users, analysts or data scientists.
  • Contribute to the organisation’s data integration strategy and roadmap: propose improvements, tools, frameworks and standards.
  • Collaborate with business units to translate emerging analytic or reporting requirements into ETL/integration deliverables.
  • Participate in sprint reviews, demos and backlog prioritisation to deliver incremental value in data engineering teams.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert proficiency with Informatica PowerCenter, IICS or Informatica Cloud data integration tools.
  • Strong SQL programming skills, including writing, optimizing and debugging complex queries, stored procedures and views.
  • Deep understanding of ETL/ELT concepts, data warehousing methodologies (Kimball, Inmon, data vault), data modelling and staging architecture.
  • Experience with relational databases (Oracle, SQL Server, Teradata) and performance tuning, indexing, partitions, query optimization.
  • Knowledge of scheduling and job orchestration tools, workflow automation, cluster processing, and environment management.
  • Proficiency with version control, build/release pipelines, repository management of ETL code and configuration.
  • Ability to apply data quality techniques, data cleansing, reconciliation, metadata management and governance.
  • Familiarity with cloud data integration or big data platforms (Snowflake, AWS/Azure, Hadoop, Databricks) and mapping modern integration patterns.
  • Strong troubleshooting skills: root cause analysis of ETL failures, resource bottlenecks, memory/buffer tuning, session failures.
  • Excellent documentation, technical specification development, data flow charting, mapping documentation and data lineage tracking.

Soft Skills

  • Analytical and problem‑solving mindset: able to analyse complex data integration issues and design robust ETL solutions.
  • Excellent verbal and written communication skills: able to collaborate with business stakeholders, architects, and technical teams.
  • Team‑oriented and mentoring mindset: capable of guiding junior developers, sharing knowledge and building cohesive engineering teams.
  • Time‑management and prioritisation skills: able to handle multiple ETL loads, deadlines, production fixes and enhancement tasks simultaneously.
  • Attention to detail and quality‑driven: ensures ETL deliverables meet high standards of completeness, accuracy and performance.
  • Adaptability and continuous learning: keeps up with emerging data integration technologies, best practices and tooling.
  • Ownership and accountability: takes responsibility for end‑to‑end ETL development, production support and data integrity.
  • Strategic thinking: aligns ETL and data integration work with broader data strategy, analytics roadmap and business objectives.
  • Collaboration and stakeholder management: works across business units, data analysts, DBAs and IT teams to deliver integrated solutions.
  • Resilience and decision‑making under pressure: able to support critical production windows, respond to failures effectively and minimise business impact.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Information Systems, Software Engineering or a related technical discipline.

Preferred Education:

  • Master’s degree in Data Engineering, Data Science, Business Analytics or related field, or certifications such as Informatica Certified Professional.

Relevant Fields of Study:

  • Computer Science
  • Information Systems / Data Systems
  • Software Engineering
  • Data Engineering & Analytics

Experience Requirements

Typical Experience Range:

  • 3‑5 years of hands‑on Informatica ETL development, data integration and data warehousing.

Preferred:

  • 5+ years of experience, including working with complex data integration environments, cloud data platforms, mentoring teammates and leading integration initiatives.
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