Lead Data Engineer (Databricks & Spark)

About the role

We are building a next‑generation analytics platform and need a Lead Data Engineer to design scalable ingestion, transformation, and governed data products on Databricks. The role combines hands‑on delivery with technical leadership and architecture to enable self‑service analytics and AI‑assisted engineering.

Key responsibilities

• Design scalable data ingestion and transformation frameworks on Databricks.
• Define and enforce best practices for medallion layers, operational excellence, and data quality.
• Build batch and streaming pipelines using Apache Spark and PySpark.
• Deliver end‑to‑end data products from ingestion through governed consumption layers.
• Develop trusted analytical datasets and semantic models for reporting and self‑service analytics.
• Lead proof‑of‑concepts and evaluate emerging capabilities in the Databricks ecosystem.
• Mentor engineers and drive reusable frameworks across teams.

Required profile

• 5+ years of data engineering experience on production‑grade platforms.
• Strong Databricks experience in enterprise environments.
• Lead‑level technical leadership skills to drive standards and mentor engineers.
• End‑to‑end delivery experience across ingestion, transformation, semantic layers, and reporting.
• Upper‑intermediate English proficiency (B2) for leading technical discussions.

Required skills

• Databricks
• Apache Spark
• PySpark
• SQL
• Scala (nice to have)

What we offer

• International projects with top brands.
• Work with global, diverse teams.
• Healthcare benefits and employee financial programs.
• Paid time off, sick leave, and upskilling opportunities.
• Unlimited access to LinkedIn Learning library (22,000+ courses).
• Global career opportunities and community involvement.
• Award‑winning culture recognized by Glassdoor, Newsweek and LinkedIn.