Data Platform Engineer

• Design, build, and maintain scalable, reliable, and high-throughput data ingestion pipelines for structured and semi-structured data.
• Implement robust and secure data lake and SQL-based storage architectures optimized for performance and cost.
• Develop and maintain internal tools and frameworks for data ingestion using Python, Golang, and SQL.
• Build and maintain inference pipelines to support model serving and near real time prediction workflows
• Develop and maintain internal tools and deployment processes to support ML development workflow , including experimentation, testing, deployment and monitoring.
• Collaborate cross-functionally with Cloud, Edge, Product, and AI teams to define data contracts, schemas, and retention policies.
• Use AWS cloud infrastructure (including Argo Workflows, S3, Lambda, Glue, Kinesis, Athena, and RDS) to support end-to-end data workflows.
• Employ Infrastructure-as-Code (IaC) practices using Terraform to manage data platform infrastructure.
• Monitor data pipelines for quality, latency, and failures using tools such as CloudWatch, SumoLogic, or DataDog.
• Build and maintain ingestion pipelines that land curated sensor and operational data into Delta tables on Databricks to support ML experimentation and model training.
• Continuously optimize storage, partitioning, and query performance across large-scale datasets.
• Participate in architecture reviews and ensure the platform adheres to security, compliance, and best practice standards.

• 5+ years of professional experience in software engineering or data engineering.
• Strong programming skills in Python and Golang.
• Deep understanding of SQL and modern data lake architectures (e.g., using Parquet, Iceberg, or Delta Lake).
• Hands-on experience with AWS services including but not limited to: S3, Lambda, Glue, Kinesis, Athena, and RDS.
• Proficiency with Terraform for automating infrastructure deployment and management.
• Hands-on experience with Databricks for ML workflows: MLflow experiment tracking and model registry, Model Serving, and building training datasets in Delta Lake (PySpark/Spark SQL).
• Experience working with real-time or batch data ingestion at scale, and designing fault-tolerant ETL/ELT pipelines.
• Familiarity with DVC for dataset versioning and management.
• Familiarity with event-driven architectures and messaging systems like Kafka or Kinesis.
• Strong debugging and optimization skills across cloud, network, and application layers.
• Excellent collaboration, communication, and documentation skills.

Bonus Points

• Experience working with time-series or IoT sensor data at industrial scale.
• Exposure to gRPC and protobuf-based data contracts.
• Experience supporting ML pipelines and feature stores.
• Working knowledge of Kubernetes concepts.
• Databricks Machine Learning certification (Professional)
• Prior startup experience and/or comfort working in fast-paced, iterative environments.