Lead Data Integration Engineer

About the role

We are looking for an experienced Lead Data Integration Engineer to head our data solutions team. In this senior technical leadership position you will design, build, and maintain enterprise‑grade data pipelines while guiding a high‑performing group of engineers.

Key responsibilities

• Mentor and lead a team of 5+ data integration and ETL engineers, managing sprint planning, resource allocation, and risk.
• Conduct code reviews and establish testing frameworks for SQL, Python, and Scala code.
• Champion modern software practices such as CI/CD, Git version control, modular design, and automated monitoring.
• Architect end‑to‑end data pipeline solutions using serverless or hybrid cloud architectures.
• Implement robust scheduling and orchestration with Apache Airflow (Astronomer) or cloud‑native schedulers to achieve >99.5% reliability.
• Design and maintain scalable data models (OLAP, OLTP, star/snowflake schemas, data lakes, lakehouses, data meshes).
• Implement CDC, micro‑batching, delta extracts, and partition maintenance.
• Collaborate with product owners, architects, and business stakeholders to translate requirements into technical specifications and data‑flow diagrams.
• Present technical architectures to C‑level executives, highlighting ROI and performance gains.

Required profile

• 5+ years of hands‑on experience in data management, database design, migration, and advanced modeling.
• Proven track record leading and mentoring teams of 4–7 data engineers.
• Strong communication skills to bridge business and technology.

Required skills

• ETL tools: Talend, AWS Glue, Azure Data Factory, GCP Dataflow, Apache NiFi.
• Orchestration: Apache Airflow (Astronomer) or equivalent cloud schedulers.
• Programming: SQL, Python (PySpark, Pandas), Scala, SparkSQL, Bash.
• Cloud platforms: AWS, Azure, GCP.
• Data modeling: OLAP, OLTP, star/snowflake schemas, data lakes, lakehouses, data mesh.
• Change Data Capture (CDC) techniques, micro‑batching, delta processing.