Lead Data Engineer

Mindlance is seeking a Lead Data Engineer to take ownership of the product backlog and partner with engineering teams to design scalable data platform capabilities. The role involves defining data ingestion patterns, aligning stakeholders, and ensuring cloud-native platform capabilities while being hands-on in technical implementations.

Responsibilities:

• Own the product backlog: decompose epics and business vision into well-defined, developer-ready features and user stories
• Partner with engineering and architecture teams to design scalable, reusable enterprise data platform capabilities
• Define and govern batch and streaming data ingestion and integration patterns across the platform
• Drive and own ceremonies of SAFe Agile / LPM, including PI planning, iteration goals, and backlog refinement
• Incorporate agentic AI and LLM-based automation into platform development workflows
• Align cross-functional stakeholders on technical tradeoffs, platform roadmap, and delivery priorities
• Ensure platform capabilities are cloud-native, scalable, and built for reuse across enterprise consumers
• Must be hands-on technical with the ability to independently write proof-of-concepts (POCs) and reference implementations to guide and accelerate the development team
• Build reusable data integration and data ingestion platform capabilities that can be leveraged by all application teams to acquire data from systems of record and make it available in the data platform
• Build capabilities to catalog acquired data from systems of record, enabling discoverability and governance across the platform
• Expected to deeply understand the "what" — the business intent and product vision — and independently derive a very detailed and technical "how" — the implementation approach, architecture decisions, and engineering specifications ready for the development team

Requirements:

• Own the product backlog: decompose epics and business vision into well-defined, developer-ready features and user stories
• Partner with engineering and architecture teams to design scalable, reusable enterprise data platform capabilities
• Define and govern batch and streaming data ingestion and integration patterns across the platform
• Drive and own ceremonies of SAFe Agile / LPM, including PI planning, iteration goals, and backlog refinement
• Incorporate agentic AI and LLM-based automation into platform development workflows
• Align cross-functional stakeholders on technical tradeoffs, platform roadmap, and delivery priorities
• Ensure platform capabilities are cloud-native, scalable, and built for reuse across enterprise consumers
• Must be hands-on technical with the ability to independently write proof-of-concepts (POCs) and reference implementations to guide and accelerate the development team
• Build reusable data integration and data ingestion platform capabilities that can be leveraged by all application teams to acquire data from systems of record and make it available in the data platform
• Build capabilities to catalog acquired data from systems of record, enabling discoverability and governance across the platform
• Expected to deeply understand the 'what' — the business intent and product vision — and independently derive a very detailed and technical 'how' — the implementation approach, architecture decisions, and engineering specifications ready for the development team
• Data Processing Python, PySpark, PyFlink, ETL/ELT batch & streaming patterns
• Data Platforms Databricks, Databricks Lakeflow, structured & unstructured data engineering
• Databases & CDC MS SQL Server, PostgreSQL, MariaDB, MongoDB (CDC patterns), Kafka Connect, Debezium
• Streaming & Eventing Confluent Kafka, AWS EventBridge, AWS Kinesis, AWS SQS, Google Pub/Sub, Azure Event Hub
• Application Development REST APIs, Python-based service development
• Cloud & Infrastructure Azure, GCP, Terraform, Kubernetes
• Agile SAFe / LPM — PI planning, epic decomposition, backlog ownership
• AI/ML Agentic AI development patterns and integration workflows