Lead Data Engineer in Newark
Role details
Job location
Tech stack
Requirements
8+ years in data engineering, with 3+ years as a tech lead owning end-to-end delivery (not a pure design/review architect role).
\n
Proven track record of shipping data platforms on committed timelines, including hands-on troubleshooting under delivery pressure.
\n
Deep expertise in relational and canonical data modeling and MDM design.
\n
Expert-level SQL and solid understanding of when NoSQL patterns are appropriate.
\n
Proven experience designing schema-first data contracts (JSON Schema/Avro/Protobuf).
\n
Experience architecting data catalog tooling and PII classification/data regulation compliance (critical given financial-services context).
\n
Strong hands-on architecture experience with Snowflake or an equivalent enterprise data warehouse.
\n
Proven design of data pipelines between operational data stores and the warehouse.
\n
Experience architecting change-data-capture and data validation/quality frameworks.
\n
Demonstrated experience implementing "contract as code" - schema management via CI/CD, Git-based governance.
\n
Solid knowledge of relevant AWS data services (e.g., RDS, Redshift, Glue, DMS, Lake Formation).
\n
Financial-services or similarly regulated-industry data governance experience is strongly .
\n
Strong stakeholder communication; able to directly manage day-to-day delivery of an offshore team (standups, unblocking, sprint accountability).
Benefits & conditions
Own delivery of relational and canonical data models and the organization's MDM (master data management) strategy.
\n
Set standards for advanced SQL usage and appropriate use of NoSQL patterns where relational modeling doesn't fit.
\n
Define schema-first data contract design using JSON Schema/Avro/Protobuf.
\n
Drive rollout of the data catalog and enforce PII classification and data-regulation compliance across pipelines.
\n
Own delivery of the Snowflake/enterprise data warehouse and data pipelines between ODS and warehouse.
\n
Drive delivery of data change detection (CDC) and the data validation/quality testing framework.
\n
Own "contract as code" governance: data schema management via CI/CD and Git-based data governance.
\n
Select and stand up appropriate AWS data services for the platform.
\n
Run day-to-day delivery of the offshore team: sprint commitments, code/design review, real-time unblocking, and hands-on work on critical-path pipelines.
\n
Report delivery status, risks, and blockers to engineering leadership.
\n
\n