Senior AWS Data Engineer

About The Opportunity We are looking for aSenior AWS Data Engineerto build and productionize data transformation layers for large-scale GA4 clickstream data. This role will focus on transforming raw data from an S3-based Bronze layer into scalable, domain-alignedSilver and Gold layers in Amazon Redshift, enabling analytics, Business Intelligence, and AI/RAG use cases.
This is a highly hands-on position focused onproduction delivery rather than prototypes. You will be responsible for building reliable data pipelines, designing analytics-ready data models, implementing data access controls, and optimizing Redshift workloads for downstream consumers such as Tableau, Power BI, and AI applications. Responsibilities Build and maintain production-grade data transformation pipelines using AWS Glue, Python, Matillion, or similar technologies. Transform and flatten nested GA4/GA360 clickstream data, including complex struct and array fields, into queryable analytical tables. Design and implement wide, denormalized, domain-aligned Silver-layer tables optimized for BI and analytical workloads. Build Gold-layer aggregates on top of Silver data to support reporting, analytics, and AI/RAG use cases. Implement column-level PII security and access controls using AWS Lake Formation, with distinct access models for BI, AI, and Data Science consumers. Develop and deploy Redshift late-binding views as the primary consumption layer for Tableau and Power BI. Ensure raw S3 data and underlying partitions remain protected from direct end-user access. Optimize Amazon Redshift workloads, including distribution keys, sort keys, workload management, and query performance. Reconcile and integrate new GA4 datasets with existing legacy clickstream data where appropriate. Define and document data lineage, table grain, business definitions, and refresh cadence for production datasets. Collaborate with technical and architectural stakeholders to make sound data architecture decisions. Ensure clean documentation, knowledge transfer, and handoff at the end of the engagement. Required Qualifications 5+ years of professional experience in Data Engineering, with recent hands-on production experience in AWS environments. Strong experience with Amazon S3, AWS Glue, AWS Lake Formation, Amazon Redshift, and Redshift Spectrum. Proven experience working with GA4 or GA360 data, particularly BigQuery-style nested and repeated structures. Strong SQL and Python skills, with experience developing production-grade data transformations. Hands-on experience with AWS Glue jobs, Matillion, or comparable data transformation frameworks. Strong knowledge of Amazon Redshift performance optimization, including distribution keys, sort keys, late-binding views, and workload management. Experience implementing column-level security and role-based access controls using AWS Lake Formation. Ability to design data models that support BI, analytics, and AI/RAG workloads, beyond traditional normalized or Kimball-style modeling. Strong understanding of data engineering best practices, including data quality, lineage, documentation, scalability, and production reliability. Ability to work independently and collaborate effectively with architects, BI teams, data scientists, and other technical stakeholders. Nice To Have Skills Experience working with airline, travel, e-commerce, or high-volume customer clickstream data. Experience with dbt or similar data transformation and modeling frameworks. Familiarity with Tableau and Power BI, particularly their semantic-layer requirements and common issues caused by poorly designed schemas. Experience preparing data for vector databases, LLM applications, or RAG pipelines. Experience feeding AI or RAG applications from Redshift Gold-layer datasets. Familiarity with modern data lakehouse and cloud data architecture patterns. Experience working with large-scale event-based datasets and complex analytical workloads.