Data Pipelines Architect

4 days ago

alappuzha, India beBeeDataEngineer Full time

Healthcare Analytics EngineerWe are creating the future of healthcare analytics by designing and building robust data pipelines that power nationwide analytics and support our machine learning systems. Our goal is to have pipelines that are reliable, observable, and continuously improving in production.This is a fully remote role open to candidates based in Europe or India with periodic team gatherings in Mountain View, California. As a Healthcare Analytics Engineer, you will be responsible for various tasks including:Designing, building, and maintaining scalable ETL pipelines using Python (Pandas, PySpark) and SQL, orchestrated with Airflow (MWAA).Developing and maintaining the SAIVA Data Lake/Lakehouse on AWS ensuring quality, governance, scalability, and accessibility.Running and optimizing distributed data processing jobs with Spark on AWS EMR and/or EKS.Implementing batch and streaming ingestion frameworks APIs, databases, files, event streams.Enforcing validation and quality checks to ensure reliable analytics and ML readiness.Monitoring and troubleshooting pipelines with CloudWatch integrating observability tools like Grafana, Prometheus, or Datadog.Automating infrastructure provisioning with Terraform following AWS best practices.Managing SQL Server, PostgreSQL, and Snowflake integrations into the Lakehouse.Participating in an on-call rotation to support pipeline health and resolve incidents quickly.The ideal candidate should have:5+ years of experience in data engineering, ETL pipeline development, or data platform roles.Experience designing and operating data lakes or Lakehouse architectures on AWS.Strong SQL skills with PostgreSQL, SQL Server, and at least one AWS cloud warehouse.Proficiency in Python (Pandas, PySpark); Scala or Java a plus.Hands-on with Spark on AWS EMR and/or EKS for distributed processing.Strong background in Airflow (MWAA) for workflow orchestration.Expertise with AWS services: S3, Glue, Lambda, Athena, Step Functions, ECS, CloudWatch.Proficiency with Terraform for IaC; familiarity with Docker, ECS, and CI/CD pipelines.Experience building monitoring, validation, and alerting into pipelines with CloudWatch, Grafana, Prometheus, or Datadog.Strong communication skills and ability to collaborate with data scientists, analysts, and product teams.A track record of delivering production-ready, scalable AWS pipelines, not just prototypes.

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