Data Engineer Jobs in Jersey City, NJ

The Big Data Engineer will develop and maintain big data solutions, collaborate in an agile team, and implement machine learning models to drive analytics projects.

Lead engineering strategy and hands-on implementation for Finance & Actuarial data platforms. Architect no-touch automated data pipelines, drive cloud migrations (Azure Synapse/Databricks/Snowflake), embed AI-assisted development and modern DevOps, establish CI/CD and runbooks, resolve complex incidents, evaluate tooling, and mentor engineering squads while communicating strategy to executive stakeholders.

Senior individual-contributor responsible for shaping enterprise data product strategy, translating business requirements into governed data assets, and driving modernization of cloud-based data platforms. Collaborates with senior stakeholders to enable analytics, AI-assisted data consumption, data governance, metadata/lineage, and self-service capabilities while ensuring scalable architecture, data quality, and regulatory readiness across data product lifecycles.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines using the Cloudera Hadoop ecosystem. Optimize Spark and platform performance, ensure data integrity and security, collaborate with data scientists and analysts, troubleshoot issues, and implement version control and CI/CD for Spark applications.

Design, develop, test, optimize, and deploy large-scale Spark/Scala data processing and ETL pipelines on Cloudera Hadoop. Collaborate with data scientists and analysts, ensure data integrity and security, troubleshoot performance, and implement version control and CI/CD for Spark applications.

Design, implement, and maintain Java-based applications to automate financial data reconciliation across relational and NoSQL sources. Build deterministic and heuristic matching algorithms, integrate with rules engines (Drools), handle errors and exceptions, collaborate with analysts and architects to resolve data gaps, and support data governance and quality initiatives.

Design, implement, and maintain Java-based applications to automate complex data reconciliation across relational and NoSQL sources. Build matching algorithms, integrate with rules engines (Drools), handle data streams, perform gap analysis, and collaborate with analysts and architects to ensure data quality.