Lead Data Engineer - Databricks/PySpark

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 Leadership 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 : 1. 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.

2. Stream & Event Processing :

• Apache Kafka for real-time event ingestion.

• Structured Streaming for continuous data processing.

• Delta Live Tables for declarative, quality-enforced pipelines.

3. 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.