Data Engineer

About Gradera Gradera defines a new category of enterprise transformation called Software-Orchestrated Services™ - where software orchestrates human expertise, digital workers, and enterprise systems to deliver governed outcomes at scale. As an AI Native Services firm, we help enterprises redesign how work gets done across operations, product, engineering, customer experience, data, and enterprise workflows to move beyond fragmented AI pilots and disconnected automation toward measurable business outcomes

Overview We are seeking skilled

Data Engineers to join our Data & Digital Twin Foundation team. You will design, build, and maintain data pipelines that power digital twin platforms, real-time operational systems, and AI/ML workloads. Working closely with data architects, simulation engineers, and ML teams, you will transform raw operational data into high-quality, governed datasets that drive intelligent decision-making.

Key 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
• 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.

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