Description

Responsibilities

• Design and lead enterprise-grade pipelines supporting both batch and streaming data processing.
• Own the full ETL/ELT lifecycle, from design through production support.
• Drive data engineering standards, best practices, and architecture across the data ecosystem.
• Translate business requirements into scalable, high-performance data solutions.
• Mentor a small team while remaining hands-on.
• Ensure data reliability, performance, governance, and cost optimization.

Qualifications

• Required
  • 10+ years of IT experience, including deep expertise in data engineering and ETL.
  • 4–6+ years of recent hands-on experience designing, building, and scaling data pipelines.
  • Strong hands-on experience with Databricks (Spark, Delta Lake, Unity Catalog) and AWS (S3, IAM, networking).
  • Highly proficient in Databricks' core architectural pattern, which combines the best elements of data lakes and data warehouses.
  • Familiarity with advanced Databricks features including Unity Catalog (for governance), Delta Live Tables (DLT for pipelines), Databricks SQL, and workflow orchestration.
  • Expertise in SQL, Python, data modeling, and distributed processing.
  • Experience with CI/CD, DevOps, and modern data architecture (lakehouse, medallion, etc.).
  • Experience working in an Agile analytics environment with close collaboration with key stakeholders to address evolving requirements.
  • Strong communication skills and the ability to translate complex technical concepts into solutions that deliver business outcomes.
  • A passion for continued learning and an interest in staying current with the rapidly evolving data and analytics space

Key Skills

Education

Bachelor's degree