Data Engineer

A second hands-on senior engineering role alongside Senior Data Engineer #1, building and maintaining production data pipelines on one cloud platform — typically covering a different workstream or cloud stack within the same data engineering team.

Key Responsibilities:

• Design, build, and maintain scalable ETL/ELT pipelines on AWS or Azure
• Optimize pipeline performance, reliability, and cost-efficiency
• Implement data-quality checks, monitoring, and alerting for production pipelines
• Collaborate with the Data Engineering Lead and Architect on pipeline design and standards
• Support data modeling and schema design together with the Data Modelling Analyst
• Troubleshoot and resolve data pipeline issues in production
• Participate in code reviews and contribute to engineering best practices

Requirements:

• 5+ years of hands-on data engineering experience
• Deep expertise in one cloud platform: AWS (Glue, S3, Redshift, EMR, Lambda, Kinesis) or Azure (Data Factory, Synapse, ADLS, Databricks)
• Strong SQL and Python (or Scala) skills; experience with orchestration tools (Airflow, ADF, etc.)
• Experience with ETL/ELT design, data-quality checks, and performance optimization
• Familiarity with distributed data processing frameworks (Spark)
• Experience operating production data pipelines at scale
• Solid understanding of data warehousing and dimensional modeling concepts

Nice to Have:

• Familiarity with CI/CD and infrastructure-as-code (Terraform)
• Experience with streaming data pipelines (Kafka, Kinesis, Event Hubs)
• Exposure to dbt or similar transformation tooling