Lead Data Platform Engineer - Enterprise, Data & AI

In This Role, You Will...

Scaling self-healing data pipelines that automatically handle schema evolution, detect anomalies, execute circuit breakers and recover from failures without manual intervention.

Define and enforce company-wide data governance, access, automated data quality testing, schema evolution policies and metadata management to maintain high-fidelity data assets.

Implement intelligent storage lifecycle strategies, resource throttling and query optimization to minimize compute overhead while delivering high-throughput, low-latency data access for analytics and AI agents. With a focus on Databricks vs EMR (AWS) cloud optimization (cost and performance).

Build automated CI/CD deployment templates, environment isolation, testing frameworks and version control standards, enabling rapid, reliable and zero-downtime deployments

Qualifications

10+ years of hands-on experience in Data Platform Engineering or Software Engineering with a proven track record of architecting and scaling production-grade data foundations.

Deep expertise in scaling and optimization, query tuning, compute resource allocation and implementing efficient compute-storage lifecycle policies to minimize infrastructure costs.

Experience implementing enterprise security standards, Role-Based Access Control (RBAC), Active Directory/IAM roles and fine-grained data masking, with strong familiarity supporting enterprise compliance requirements (e.g., SOX, audit trail controls, data retention policies).

Track record of establishing enterprise data governance frameworks, automated schema evolution controls, data quality audits and near real-time observability/alerting.

Hands-on experience building automated CI/CD pipelines, environment isolation (branch testing, rollback mechanisms), version control and automated deployment testing for data assets.

Bonus Qualifications

Experience incorporating LLMs or GenAI directly into pipeline operations (e.g., code generation, triage and root-cause analysis, automated data reconciliation, backfills or anomaly detection).

Experience with infrastructure-as-code (Terraform) and containerization/orchestration (EKS) for elastic compute management. GPU/CPU performance optimization & utilization.

Previous experience in the autonomous vehicle, robotics or high-tech manufacturing sectors.