Data Engineer

I'm building a world-class team to power our next generation of data products. We're looking for a Senior Data Engineer who knows AWS inside and out—someone who can design secure, scalable data pipelines, own ETL/ELT workflows, engineer cloud data infrastructure, and deliver dimensional and semantic models that our analysts, data scientists, and applications can trust.

You'll work closely with product, security, platform engineering, and analytics to move our architecture toward a real-time, governed, cost-aware, and highly automated data ecosystem.

What You'll DoDesign & build end-to-end pipelines on AWS (batch and streaming) using services like Glue, EMR, Lambda, Step Functions, Kinesis, MSK, and Fargate.

Develop robust ETL/ELT (PySpark, Spark SQL, SQL, Python) for structured, semi-structured, and unstructured data at scale.

Own data storage & processing layers: S3 (Lake/Lakehouse), Redshift (or Snowflake on AWS), DynamoDB, and Athena with strong partitioning, compaction, and performance tuning.

Implement data models (3NF, dimensional/star, Data Vault, Lakehouse medallion) for analytics and operational workloads.

Engineer secure infrastructure-as-code with Terraform (or CDK) across multi-account setups; implement CI/CD via GitHub Actions or AWS CodeBuild/CodePipeline.

Harden security & governance: use IAM, Lake Formation, KMS, Secrets Manager, VPC/PrivateLink, GLUE Catalog, and fine-grained access controls. Partner with SecOps on compliance (e.g., SOC 2, Fed

RAMP, HIPAA

depending on dataset).Observability & reliability: build monitoring with CloudWatch, OpenTelemetry, and data quality checks (e.g., Great Expectations, Deequ), implement SLOs and alerts.

Champion best practices: code reviews, testing (unit/integration), documentation, runbooks, and blameless postmortems.

Mentor mid-level engineers and collaborate on architectural decisions, standards, and technical roadmaps.