Senior Data Platform Specialist

14 hours ago


bikaner, India beBeeData Full time

Senior Data Engineer PositionWe are seeking a highly skilled Senior Data Engineer to join our analytics team. As a key member of our team, you will play a vital role in designing, building, and scaling robust data pipelines that power nationwide analytics and support our machine learning systems.This is a fully remote position, open to candidates worldwide, with opportunities for collaboration with cross-functional teams.Job DescriptionThe successful candidate will be responsible for designing and maintaining scalable ETL pipelines using Python (Pandas, PySpark) and SQL, orchestrated with Airflow (MWAA). They will also develop and maintain the SAIVA Data Lake/Lakehouse on AWS, ensuring quality, governance, scalability, and accessibility.Key responsibilities include 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.Requirements5+ years in data engineering, ETL pipeline development, or data platform roles (flexible for exceptional candidates).Experience designing and operating data lakes or Lakehouse architectures on AWS (S3, Glue, Lake Formation, Delta Lake, Iceberg).Strong SQL skills with PostgreSQL, SQL Server, and at least one AWS cloud warehouse (Snowflake or Redshift).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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