Senior Data Engineer (Spark / dbt)

About the role

What you’ll be doing

We're hiring a Senior Data Engineer for our Data Practice to design and build scalable data platforms — pipelines, warehouses, and transformation layers — that power analytics and AI initiatives for enterprise clients across finance, healthcare, and commerce.

• Design and build batch and streaming data pipelines using Spark and modern orchestration tools
• Own dbt-based transformation layers, ensuring tested, documented, and reliable data models
• Architect data warehouse schemas optimized for analytics and downstream ML workloads
• Implement data quality monitoring, lineage tracking, and observability across pipelines
• Partner with AI Practice engineers to productionize feature stores and training datasets
• Optimize pipeline performance and cost across large-scale distributed data processing
• Document data architecture decisions and mentor junior data engineers

• 5+ years of data engineering experience with large-scale distributed systems
• Strong hands-on expertise with Apache Spark and dbt in production environments
• Proficiency in SQL and Python for pipeline development and data modeling
• Experience with modern data warehouses (Snowflake, BigQuery, or Redshift)
• Familiarity with workflow orchestration tools (Airflow, Dagster, or similar)
• Understanding of data governance, quality, and lineage best practices
• Consulting or multi-client delivery experience is a plus