Lead Data Engineer

Role & responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Delta Lake
• Build real-time and batch data ingestion pipelines from diverse operational systems using high-performance Kafka data pipelines.
• Implement data transformations that serve digital twin platforms and operational analytics
• 2+years of Technical Leader ship Experience
• Integrate Kafka event streams with Databricks for real-time operational state updates
• Implement data quality checks using Delta Live Tables expectations
• Ensure data governance compliance through Unity Catalog (lineage, access control, metadata)
• Optimize pipeline performance, reliability, and cost efficiency
• Write clean, well-documented, and testable code following engineering best practices
• Collaborate with ML engineers to deliver feature-engineered datasets
• Participate in code reviews, knowledge sharing, and continuous improvement initiatives
• Support production data systems through monitoring, troubleshooting, and incident resolution.
• Build business data warehouse solutions using Terradata for business intelligence.

Preferred candidate profile

Our core data platform stack includes:

Data Platform & Lakehouse

• Databricks as the single point of truth for all data
• Realtime Data Pipelines implemented using Kafka for data ingestion.
• Databricks SQL for analytical queries
• Unity Catalog for metadata management and governance
• Terradata for data warehouse and business intelligence.

Stream & Event Processing

• Apache Kafka for real-time event ingestion
• Structured Streaming for continuous data processing
• Delta Live Tables for declarative, quality-enforced pipelines

Data Quality

• Delta Live Tables expectations for data validation
• Data profiling and anomaly detection

Preferred Qualifications

• 7+ years of hands-on data engineering experience
• Track record of building and maintaining production-grade data pipelines
• Experience with Delta Live Tables for declarative pipeline development
• Experience working in agile, cross-functional teams
• Familiarity with time-series data patterns and operational data modelling

Highly Desirable

• Experience building data pipelines for digital twin or simulation platforms
• Familiarity with operational state modeling for real-time systems
• Exposure to physics-informed or time-series ML feature engineering
• Experience working with distributed, multidisciplinary teams
• Exposure to industrial domains such as Manufacturing, Logistics, or Transportation is a plus