<p>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 <strong>design secure, scalable data pipelines</strong>, <strong>own ETL/ELT workflows</strong>, <strong>engineer cloud data infrastructure</strong>, and <strong>deliver dimensional and semantic models</strong> that our analysts, data scientists, and applications can trust.</p><p>You’ll work closely with product, security, platform engineering, and analytics to move our architecture toward a <strong>real-time, governed, cost-aware</strong>, and <strong>highly automated</strong> data ecosystem.</p><p><strong>What You’ll Do</strong></p><ul><li><strong>Design &amp; build end-to-end pipelines</strong> on AWS (batch and streaming) using services like <strong>Glue, EMR, Lambda, Step Functions, Kinesis, MSK</strong>, and <strong>Fargate</strong>.</li><li><strong>Develop robust ETL/ELT</strong> (PySpark, Spark SQL, SQL, Python) for structured, semi-structured, and unstructured data at scale.</li><li><strong>Own data storage &amp; processing layers</strong>: <strong>S3 (Lake/Lakehouse), Redshift (or Snowflake on AWS), DynamoDB</strong>, and <strong>Athena</strong> with strong partitioning, compaction, and performance tuning.</li><li><strong>Implement data models</strong> (3NF, dimensional/star, Data Vault, Lakehouse medallion) for analytics and operational workloads.</li><li><strong>Engineer secure infrastructure-as-code</strong> with <strong>Terraform</strong> (or <strong>CDK</strong>) across multi-account setups; implement CI/CD via <strong>GitHub Actions</strong> or <strong>AWS CodeBuild/CodePipeline</strong>.</li><li><strong>Harden security &amp; governance</strong>: use <strong>IAM</strong>, <strong>Lake Formation</strong>, <strong>KMS</strong>, <strong>Secrets Manager</strong>, <strong>VPC/PrivateLink</strong>, <strong>GLUE Catalog</strong>, and fine-grained access controls. Partner with SecOps on compliance (e.g., <strong>SOC 2</strong>, <strong>FedRAMP</strong>, <strong>HIPAA</strong> depending on dataset).</li><li><strong>Observability &amp; reliability</strong>: build monitoring with <strong>CloudWatch</strong>, <strong>OpenTelemetry</strong>, and data quality checks (e.g., <strong>Great Expectations</strong>, <strong>Deequ</strong>), implement SLOs and alerts.</li><li><strong>Champion best practices</strong>: code reviews, testing (unit/integration), documentation, runbooks, and blameless postmortems.</li><li><strong>Mentor</strong> mid-level engineers and collaborate on architectural decisions, standards, and technical roadmaps.</li></ul><p><br></p>

<p><strong>Core Tech You’ll Use</strong></p><ul><li><strong>AWS</strong>: S3, Glue, EMR, Lambda, Step Functions, Kinesis/MSK, Redshift, Lake Formation, IAM, KMS, Secrets Manager, CloudWatch</li><li><strong>Data</strong>: PySpark/Spark, SQL, Python, dbt (nice to have), Kafka, Airflow</li><li><strong>IaC/CI/CD</strong>: Terraform (or CDK), GitHub Actions / CodePipeline</li><li><strong>Quality/Governance</strong>: Great Expectations/Deequ, Glue Catalog, Data Contracts, Delta/Apache Hudi/Iceberg (plus)</li></ul><p><strong>What Makes You a Great Fit</strong></p><ul><li>5–10+ years in data engineering with at least <strong>3+ years deep on AWS</strong>.</li><li>Hands-on with <strong>Spark/PySpark</strong>, <strong>distributed ETL</strong>, and <strong>data modeling</strong> for analytics (dimensional) and/or operational use.</li><li>Strong command of <strong>SQL performance tuning</strong>, partitioning, file formats (<strong>Parquet/ORC/Avro</strong>), and cost/performance trade-offs.</li><li>Proven <strong>security-first mindset</strong>: least privilege, secrets rotation, encryption at rest/in transit, network boundaries.</li><li>Experience building <strong>production-grade</strong> data platforms with IaC and CI/CD.</li><li>Excellent communicator who can translate business goals into data architecture.</li></ul><p><strong>Nice-to-Haves</strong></p><ul><li>Experience with <strong>Redshift RA3</strong> tuning, <strong>Delta/Hudi/Iceberg</strong>, <strong>Data Mesh</strong> patterns, or <strong>dbt</strong>.</li><li>Knowledge of <strong>ML feature stores</strong> (SageMaker Feature Store or equivalent).</li><li>Background in <strong>regulated environments</strong> (public sector, healthcare, financial services).</li><li>Active <strong>Public Trust</strong> or ability to obtain a clearance (preferred but not required).</li></ul><p><br></p>

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