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Job Description
Own the data platform for a greenfield AWS predictive maintenance system spanning 4 global cities. Build ingestion pipelines, Medallion Architecture on Databricks, and Glue + Step Functions orchestration — feeding ML models on SageMaker .
Must-Have Skills : AWS (5+ yrs), Spark/Databricks (4+ yrs), AWS Glue + Step Functions (3+ yrs), Medallion Architecture, Python & SQL (5+ yrs), PostgreSQL/SQL Server + Redis, Unity Catalog/Lake Formation, and Git/CI-CD.
Nice-to-Have: EKS/Kubernetes, Grafana, data quality frameworks, AWS or Databricks certifications.
Experience: 7-15 years total, with 5+ on AWS and 4+ on Databricks/Spark. Must have owned production pipelines with SLAs at scale.
Responsibilities
- Build ingestion pipelines and Medallion Architecture (Bronze → Silver → Gold) on Databricks from multiple city data sources
- Orchestrate ETL workflows using AWS Glue 3.0 + Step Functions across auto-scaling Spot clusters
- Engineer feature sets powering 80 SageMaker ML models across cities and device types
- Own all data platform infrastructure
- Partner with ML engineers on retraining pipelines, data quality, and platform scaling
Qualifications
Anyone with the below skills:
Must-Have Skills : AWS (5+ yrs), Spark/Databricks (4+ yrs), AWS Glue + Step Functions (3+ yrs), Medallion Architecture, Python & SQL (5+ yrs), PostgreSQL/SQL Server + Redis, Unity Catalog/Lake Formation, and Git/CI-CD.
Nice-to-Have: EKS/Kubernetes, Grafana, data quality frameworks, AWS or Databricks certifications.
Experience: 7-15 years total, with 5+ on AWS and 4+ on Databricks/Spark. Must have owned production pipelines with SLAs at scale.
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