Key Responsibilities and Required Skills for Data Integration Developer

🎯 Role Definition

As a Data Integration Developer, you will design, implement and maintain data integration solutions that support the flow of information across diverse systems and applications. You will work closely with data architects, analysts, IT infrastructure teams and business stakeholders to create scalable, reliable data pipelines, ETL/ELT processes, APIs, and middleware that deliver accurate, timely, and high‑quality data. You are responsible for data movement, transformation, harmonisation, quality assurance, testing, monitoring and documentation – enabling data‑driven decision‑making within the organisation.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior ETL/Integration Developer
Data Engineer (Entry Level)
Business Intelligence Developer

Advancement To:

Senior Data Integration Developer / Lead Integration Engineer
Data Integration Architect
Manager – Data Engineering or Head of Data Platforms

Lateral Moves:

API / Systems Integration Developer
Data Platform Engineer
Middleware / Enterprise Service Bus (ESB) Engineer

Core Responsibilities

Primary Functions

Design, develop and maintain robust data integration pipelines and ETL/ELT processes that extract, transform and load data across source and target systems.
Collaborate with data architects, business analysts and stakeholders to understand integration requirements, data flows and business logic, then translate them into technical specifications.
Perform source‑to‑target mapping, data modelling and transformation logic to ensure data consistency, integrity, and quality across systems.
Implement, test and validate data integration processes and middleware to ensure correct, efficient and secure data exchange between internal and external systems.
Develop and maintain data integration interfaces (APIs, web services, message queues, batch jobs) to enable interoperability and data flow between applications.
Optimize integration solutions for performance, scalability, reliability and maintainability, including monitoring jobs, tuning queries and improving workflows.
Monitor and diagnose production data integration issues such as failed transfers, data inconsistencies, bottlenecks or mapping errors; apply corrective actions and preventive improvements.
Maintain version control, documentation and metadata for integration artifacts, workflows, mappings, transformations and data lineage.
Create and execute integration test plans including unit, integration and end‑to‑end data validation to ensure robust delivery of data solutions.
Manage deployment and scheduling of integration jobs, ensure timely execution, handle cutover plans, and support system upgrades or migrations.
Ensure data governance, security and compliance requirements are embedded in integration solutions, including encryption, role‑based access, and auditability.
Work with cloud, on‑premises or hybrid environments to design data integration solutions that support modern data architecture and enterprise data platforms.
Provide technical support, incident resolution and maintenance for integration services operating in production, and assist service desk or infrastructure teams as needed.
Participate in agile development cycles: planning, backlog grooming, sprint execution, retrospectives and continuous improvement of integration practices.
Communicate effectively across teams: coordinate with data scientists, data analysts, application owners and business users to ensure data requirements are met.
Lead or contribute to integration architecture decisions, including selection of tools, middleware, data formats and integration patterns.
Review and refactor existing integration code, data flows and ETL frameworks to reduce technical debt and improve maintainability and performance.
Document and support metadata, data lineage, data cataloguing and impact analyses to enhance transparency and audibility of data flows.
Stay current with integration technologies, ETL/ELT trends, cloud data platforms and middleware solutions; propose and implement improvements accordingly.
Mentor junior integration developers, share best practices, conduct knowledge sharing sessions and promote integration frameworks and standards.

Secondary Functions

Support ad‑hoc data extraction, profiling, or exploratory analysis tasks that feed into integration or analytics workflows.
Contribute to the organisation’s data integration roadmap, recommending tool upgrades, migration strategies and process optimisation.
Collaborate with business units to translate integration requirements into engineering tasks and backlog items.
Participate in agile ceremonies and contribute to continuous improvement of integration methodologies and delivery cadence.

Required Skills & Competencies

Hard Skills (Technical)

Proficiency in ETL/ELT tools and data integration platforms (e.g., SSIS, Talend, Informatica, Azure Data Factory).
Strong experience in SQL and working with relational databases (SQL Server, Oracle, PostgreSQL) and writing optimized queries, stored procedures and data transformations.
Experience creating and maintaining APIs, web services and middleware for data exchange (RESTful, SOAP, message queues).
Expertise in data mapping, data modelling, source‑to‑target reconciliation, data cleansing and transformation logic.
Experience deploying, scheduling, monitoring and supporting integration workflows in production environments, including job orchestration and alerting.
Familiarity with cloud data platforms, hybrid architectures, and integration between on‑premises and cloud systems.
Knowledge of data governance, data quality frameworks, metadata management, and data lineage documentation.
Ability to optimize performance of data pipelines and integration services — tune ETL jobs, databases, memory and networking.
Experience with version control, CI/CD pipelines, deployment automation and auditing of integration artefacts.
Familiarity with scripting or programming languages (e.g., Python, C#, Java) used to support integration logic, automation, or middleware.

Soft Skills

Excellent analytical and problem‑solving ability: able to interpret data flows, troubleshoot integration issues, and propose durable solutions.
Strong communication and collaboration skills: able to work with technical and non‑technical stakeholders, translate business needs into integration requirements.
Detail‑oriented and disciplined: capable of creating accurate documentation, managing configuration, versioning and maintaining high data quality.
Time‑management and organisational skills: able to prioritise tasks, manage multiple pipelines and deliver integration features on schedule.
Adaptability: thrives in agile, fast‑changing environments and able to respond to evolving data architecture demands.
Mentoring mindset: willing to share knowledge, coach junior engineers, and promote best practices in integration development.
Business‑ and data‑centric mindset: understands how data integration supports analytics, reporting and operational objectives.
Continuous‑learning orientation: stays current with integration tools, cloud platforms and data engineering trends.
Accountability and ownership: takes responsibility for end‑to‑end integration delivery, monitors production health and owns remediation.
Stakeholder engagement: engages proactively with business and IT teams to align data flows and integration deliverables with business strategy.

Education & Experience

Educational Background

Minimum Education:
Bachelor’s degree in Computer Science, Information Systems, Software Engineering, Data Engineering or a related technical discipline.

Preferred Education:
Master’s degree or advanced certification in Data Engineering, Integration Technologies, Cloud Data Platforms or ETL/ELT systems.

Relevant Fields of Study:

Computer Science or Software Engineering
Information Systems / Data Engineering
Data Warehousing / Business Intelligence
Cloud Computing / Data Platforms

Experience Requirements

Typical Experience Range:
2‑5 years of hands‑on experience in data integration or ETL development, data pipelines, middleware or related data engineering roles.

Preferred:
5+ years of experience designing, developing and maintaining enterprise‑scale data integration platforms, managing complex data flows, mentoring others and working with hybrid cloud/on‑premises architectures.