Lead Data Integration Engineer

Responsibilities

• Lead: Guide a team of data integration and ETL engineers, fostering a collaborative, innovation-driven culture.
• Manage sprint planning, resource allocation, and risk management
• Code Review & Quality Assurance
• Conduct regular code reviews and set up testing frameworks to ensure compliant, highly performant, and secure database code (SQL, Python, Scala)
• Culture of Excellence
• Act as a role model, championing modern software development practices (CI/CD, version control via Git, modular code design, automated alerting, and monitoring)
• Modern ETL/ELT
• Design: Architect end-to-end data pipeline solutions using serverless frameworks or hybrid setups
• Orchestration & Automation: Establish robust pipeline scheduling and workflow orchestration using tools like Apache Airflow (Astronomer) or cloud-native schedulers to achieve high pipeline reliability (>99.5%) and minimize troubleshooting times
• Data Modeling & Warehousing

Languages

Required

• Data Integration & ETL Tools
• Deep hands-on experience with major ETL tools such as Talend, AWS Glue, Azure Data Factory, GCP Dataflow, or Apache NiFi
• Strong working experience with orchestration and scheduling engines, particularly Apache Airflow (Astronomer)
• Strong production coding skills in SQL, Python (PySpark, Pandas), Scala, SparkSQL, or Bash
• Cloud Platforms: Extensive knowledge of at least one major cloud platform (AWS, Azure, GCP) with emphasis on big data warehousing engines (such as Redshift, Snowflake, Google BigQuery, or Azure Synapse)
• Proven understanding of cloud security structures (identity and access management, data masking, and RLS) and compliance standards (GDPR, HIPAA, PI)

Nice to have

• Leadership
• Demonstrated experience leading, mentoring, and developing teams of data engineers
• Documentation
• Communication & English
• Serverless Engineering
• AI/ML Integration

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