Senior Data Engineer

Our client is seeking a Senior Data Engineer to join its enterprise data platform team. This team builds and operates a multi-account AWS environment supporting more than a dozen data product teams across the organization. The platform spans data ingestion, transformation, governance and analytics using AWS Glue, Athena, Lake Formation, Redshift, S3 and Apache Iceberg.

This is a fully remote, long-term opportunity for an experienced engineer who can quickly take ownership of production data pipelines and analytics datasets.

• Design, build and optimize data pipelines using AWS Glue, PySpark and Apache Iceberg
• Own data product delivery from source-system ingestion through transformation, quality validation and governed consumption
• Build and maintain Amazon Redshift integrations, including schemas, stored procedures, materialized views and Liquibase-managed DDL migrations
• Write and optimize complex SQL for analytics transformations, reporting views and data-quality checks
• Translate operational data models into star schemas and other dimensional models for analytics
• Manage Apache Iceberg tables, including partition and schema evolution, compaction and orphan-file cleanup
• Implement table- and column-level governance using Lake Formation permissions, tag-based access control and PII classification
• Develop data-quality frameworks covering referential integrity, row-count reconciliation, completeness and anomaly detection
• Partner with data product teams to onboard new data sources, define schemas and establish data contracts
• Troubleshoot AWS Glue jobs, DMS replication issues and data-freshness problems

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• Own the complete lifecycle of changes, including development, deployment, validation and communication
• Ensure pipelines and datasets remain accurate, reliable and healthy across environments

• At least five years of hands-on data engineering experience building ETL/ELT pipelines and analytics platforms
• Expert-level SQL skills, including complex joins, CTEs, window functions, query optimization and performance tuning
• Strong Python and PySpark development experience
• Hands-on AWS Glue experience, including job development, bookmarks, crawlers and performance optimization
• Strong Amazon Redshift experience, including schema design, stored procedures, Spectrum, external schemas, materialized views and performance tuning
• Experience with Apache Iceberg or another open table format, including ACID transactions, schema evolution, partition evolution and table maintenance
• Strong understanding of dimensional data modeling, including star schemas, snowflake schemas and slowly changing dimensions
• Experience with AWS data services such as Athena, Lake Formation, S3, DMS, Lambda and EventBridge
• Understanding of data governance, tag-based access control, data classification and PII handling

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• Experience with Liquibase or a similar database migration tool
• Familiarity with GitHub and CI/CD pipelines
• Strong attention to data correctness, completeness and freshness
• Ability to independently deliver production-ready data pipelines with limited oversight

• Data Mesh architecture and federated governance
• Lakehouse architecture patterns
• AWS DMS and change-data-capture ingestion
• Terraform or other infrastructure-as-code tools
• Data catalog and lineage platforms such as AWS DataZone or IDERA ER/Studio
• AI-assisted development tools such as Amazon Kiro or GitHub Copilot
• Amazon SageMaker or other machine-learning platforms
• Docker and container-based development
• Agriculture, retail or other large-scale operational data environments