Data Engineer

Location: Remote (United States) Employment Type: contract About the Role As a Data Engineer, you’ll build and operate the pipelines, tables, and services that power our hybrid data platform across on-premises and AWS. You’ll implement lakehouse patterns, productionize batch and streaming workloads, and partner with security, platform, analytics, and application teams to deliver governed, high-performance data products at scale. What You’ll Do • Build data pipelines: Develop reliable ELT/ETL jobs in Spark/SQL/dbt/Airflow/Glue to ingest from on-prem and cloud sources into S3-backed lakes and warehouses. • Implement lakehouse tables: Create and maintain Iceberg (or Delta/Hudi) tables using the appropriate catalog (AWS Glue, Hive Metastore, Polaris/REST) with ACID, time travel, and schema evolution. • Operate compute engines: Run and tune Spark (Databricks/EMR), Trino/Starburst, Dremio, and Snowflake workloads; leverage pushdown and query acceleration where applicable. • Model data for analytics: Deliver dimensional models, semantic layers, and domain-oriented data products; document contracts and SLAs with consumers. • Governance & security: Apply data cataloging, lineage, PII classification, Lake Formation permissions, IAM roles, and row/column-level security; contribute to audit readiness. • Performance & cost tuning: Optimize partitioning, clustering/Z-order, predicate pushdown, file sizing/compaction, caching, and workload isolation; monitor and right-size clusters. • Migrations: Execute migration workstreams from legacy/on-prem EDW (Informatica/SSIS/SAP BW, SQL Server/Oracle, Hadoop) to lakehouse patterns (Spark/dbt/ELT), including dual-run cutovers and reconciliation. • Streaming & CDC: Build real-time and near-real-time pipelines using Kafka/MSK/Kinesis and Spark Structured Streaming; implement CDC with Debezium, DMS, or Fivetran. • Quality & observability: Add unit/integration tests, expectations/rules, data contracts, lineage, alerting, and SLO dashboards; participate in on-call rotations. • Platform guardrails: Contribute to standards for naming, zones, schemas, S3 layout, encryption, backup/DR, and multi-region replication; write clear runbooks and docs. • DevOps for data: Use Terraform/CloudFormation and CI/CD (GitHub Actions/GitLab/Azure DevOps) to version, test, and deploy data assets. Required Experience • 5+ years in data engineering (or equivalent), delivering production pipelines and tables on at least two large-scale platforms. • Hands-on with AWS data services: S3, Glue/EMR, Lake Formation, IAM, and at least one warehouse (Snowflake or Redshift). • Deep experience with Apache Spark and at least one of: Databricks, EMR Spark, Snowflake Snowpark, Dremio, or Starburst/Trino. • Production experience with open table formats: Apache Iceberg (preferred), Delta Lake, or Apache Hudi; strong grasp of metadata/manifest, compaction, and schema evolution. • Comfortable with on-prem stacks: Hadoop/Hive, Spark on Kubernetes, SQL Server/SSIS and/or Oracle/Exadata; Netezza/Teradata a plus. • Proven data modeling (3NF, dimensional, Data Vault), ELT/ETL design, and SQL performance tuning. • Security/governance: RBAC/ABAC, row/column-level security, masking/tokenization, KMS/key management. • IaC & CI/CD for data workloads. • Excellent communicator who can collaborate with platform engineers, analysts, and stakeholders to meet SLAs and roadmap goals. Nice to Have • dbt for ELT, Airflow orchestration, Great Expectations/Deequ for quality, OpenLineage/Marquez for lineage. • Streaming experience with Kafka/MSK, Kinesis, or Flink. • Catalogs/semantic: AWS Glue Data Catalog, Unity Catalog, Amundsen/DataHub, Atlan/Collibra. • BI/serving: DuckDB, Athena, QuickSight, Tableau/Power BI/Looker. • Compliance: SOC 2, HIPAA/PHI, GDPR, PCI; SSO/OIDC with Okta. • Multi-tenant platforms or federated governance exposure. Location & Work Style Remote with core hours in PST/CST;