Key Responsibilities and Required Skills for ETL Developer

🎯 Role Definition

As an ETL Developer, you will design, build and maintain data integration pipelines that extract, transform and load data from diverse sources into centralized data warehouses, lakes or analytical systems. You will collaborate with business analysts, data engineers, database administrators and stakeholders to ensure data quality, scalability, performance and consistency. By owning the end‑to‑end ETL lifecycle — including data mapping, modeling, optimization and documentation — you help enable accurate reporting, analytics and business intelligence capabilities across the organization.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst with SQL / ETL exposure
Database Developer or BI Developer
Junior ETL Engineer

Advancement To:

Senior ETL Developer or Lead ETL Engineer
Data Integration Architect or Data Platform Lead
Director of Data Engineering / Head of Data Platforms

Lateral Moves:

Data Engineer (with broader pipelines / streaming focus)
BI Developer / Analytics Engineer
Data Warehouse Architect

Core Responsibilities

Primary Functions

Design and implement efficient and scalable ETL pipelines to extract data from heterogeneous data sources (relational databases, flat files, APIs) and load into data warehouse or data‑lake platforms.
Collaborate with business stakeholders and data analysts to gather requirements, translate business processes into data flows and develop data mapping documents and transformation logic accordingly.
Develop detailed data models, design schemas (star, snowflake) and work with data architecture teams to ensure the warehouse supports business intelligence and analytics needs.
Build, test and deploy ETL workflows using industry‑standard tools (such as SSIS, Informatica, Talend, AWS Glue) or custom scripts (Python, SQL) to support data load, cleansing, validation and transformation.
Perform data profiling, data‑quality checks, data cleansing and reconciliation to ensure accuracy, completeness and consistency of data entering the warehouse environment.
Optimize ETL job performance: tune SQL queries and stored procedures, streamline transformations, reduce latency, improve throughput and handle large‑volume data sets efficiently.
Monitor, troubleshoot and support production ETL workflows: detect failures, diagnose root‑cause, correct issues, implement retry logic, and track key metrics (job durations, failures, volume).
Maintain version control, scheduling and automation of ETL processes (using tools like Autosys, Control‑M, Airflow) and integrate with DevOps/CI‑CD pipelines when required.
Ensure documentation is up to date: technical specifications, data‑flow diagrams, source‑to‑target mappings, transformation logic, dependencies, and operational run‑books.
Enforce data governance, compliance, security and privacy standards: mask sensitive data, maintain audit trails, ensure lineage and meet regulatory requirements (GDPR, HIPAA) when pertinent.
Collaborate with database administrators to create and maintain database objects (tables, views, indexes, partitions) required by ETL processes and ensure efficient storage design.
Refactor and enhance existing ETL frameworks, standardize mapping libraries, modularize pipelines and reduce technical debt in legacy integration systems.
Participate in data migration initiatives: move data from legacy systems or on‑premise to cloud‑data warehouses, convert formats and validate accuracy post‑migration.
Work with analytics, BI and data‑science teams to support their needs for data availability, provide staging layers, aggregated data sets and insights on data‑pipeline readiness.
Define and enforce job scheduling, monitoring dashboards, alerting and SLA metrics for ETL processes to ensure reliability and operational stability.
Conduct impact analysis and change control: assess how changes in source systems, transformations or schemas affect the data pipeline, ensure testing and deployment are managed carefully.
Mentor junior ETL developers: review their code, provide guidance on best practices, share knowledge on transformations, data modeling and tools/techniques.
Participate in agile teams: estimate tasks, refine user stories, conduct code reviews, collaborate in sprint planning and retrospectives to deliver high‑quality ETL features on schedule.
Stay current with emerging technologies in data integration, cloud‑native ETL/ELT, streaming data and big data ecosystems; propose improvements to the ETL platform and tools.
Ensure that the ETL‑developed pipelines are easily maintainable, documented, reusable and support future‑proofed architecture capable of scaling with business growth.

Secondary Functions

Support ad‑hoc data requests and exploratory data‑analysis for business units or BI teams needing rapid ingestion or transformation of data sets.
Contribute to the organization’s data‑integration strategy and roadmap: define tool‑chain standards, reusable pipeline templates, governance and architecture.
Collaborate with business units (finance, operations, marketing) to translate their data‑needs into ETL‑engineering tasks and prioritise accordingly.
Participate in sprint‑planning, daily stand‑ups and retrospectives within the data‑engineering or BI‑team.

Required Skills & Competencies

Hard Skills (Technical)

Strong proficiency in SQL, including complex queries, stored procedures, data manipulation and performance‑tuning.
Hands‑on experience with ETL tools or platforms (e.g., SSIS, Informatica PowerCenter, Talend, AWS Glue) or scripting languages (Python, Shell, Java) for custom ETL workflows.
Solid understanding of data‑warehousing concepts: dimensional modeling, star/snowflake schema designs, data marts, OLAP/ODS architectures.
Experience extracting data from structured and semi‑structured sources (flat files, APIs, CSV, JSON, XML) and loading into target systems.
Proven ability to design and build scalable ETL pipelines, batch or real‑time, handling large data volumes efficiently.
Proficiency in performance‑tuning, ETL job optimization, query tuning, indexing strategies, partitioning and resource management.
Familiarity with scripting or programming languages (Python, Bash, Perl) to automate ETL workflows and performing data transformations.
Knowledge of version‑control systems (Git), job scheduling tools (Autosys, Control‑M, Airflow) and pipeline orchestration.
Experience documenting data‑flows, mappings, transformation logic, dependencies, lineage and maintaining technical run‑books.
Awareness of data‑governance, security, compliance, and ability to implement safeguards for sensitive data in ETL workflows.

Soft Skills

Excellent verbal and written communication: able to interface with business stakeholders, data analysts, and technical teams to clarify requirements and deliver solutions.
Strong analytical and problem‑solving mindset: able to identify root‑cause of data issues, transform requirements into technical solutions, and handle complex integrations.
Ownership and accountability: responsible for the end‑to‑end ETL development lifecycle — from design through production support, performance and maintenance.
Collaboration and teamwork: works effectively with data engineers, BI analysts, DBA’s, and cross‑functional teams in a fast‑paced data‑driven environment.
Adaptability and continuous learning: stays updated with new data integration tools, cloud platforms, streaming ETL/ELT patterns and contributes to process improvements.
Time‑management and prioritisation: manages multiple ETL tasks, enhancements, data migrations and production support within tight deadlines and shifting priorities.
Mentoring and leadership: supports junior team members, holds peer reviews, promotes best practices and fosters a culture of quality in data‑integration.
User‑centric focus: understands business requirements, data‑consumer needs, analytics and reporting goals to deliver reliable and accessible data.
Quality mindset: ensures that delivered ETL solutions are maintainable, testable, documented and scalable rather than quick one‑offs.
Strategic thinking: aligns ETL efforts with broader data‑strategy, analytics roadmap and business‑goals for actionable insights and business value.

Education & Experience

Educational Background

Minimum Education:
Bachelor’s degree in Computer Science, Information Systems, Software Engineering or a related field.
Preferred Education:
Master’s degree in Data Science, Analytics, Business Intelligence or a related technical discipline is advantageous.
Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Analytics
Information Systems
Business Intelligence / Data Warehousing

Experience Requirements

Typical Experience Range:
3 – 5 years of hands‑on experience designing, building and supporting ETL pipelines, data integration or warehousing.
Preferred:
5+ years of experience with enterprise data‐warehousing, large‑scale ETL/ELT, cloud data platforms, leading data‑integration projects and mentoring others.

Key Responsibilities and Required Skills for ETL Developer