Data Engineer Jobs in Jersey City, NJ

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Lead security data architecture and governance for global security observability. Design and manage data pipelines, lakes/warehouses, and analytics (AI/ML) across SIEM/EDR/DLP sources. Publish standards, onboard applications, produce KPIs/reports, and collaborate with architecture and engineering to deliver a multi-year roadmap and threat-hunting capabilities.

Lead design, development, and operation of large-scale Spark-based data pipelines and lakehouse solutions. Write and review Python/PySpark and Spark SQL production code, optimize Spark workloads on EMR/Databricks, manage Apache Iceberg table operations, drive AI-assisted engineering adoption, automate reliability and CI/CD practices, and lead technical evaluations and communities of practice.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Provides technical leadership for Finance, Actuarial, and Corporate Data platforms. Defines engineering strategy and standards, architects automated data pipelines, leads cloud migrations and ETL modernization, and drives AI-assisted development, DevOps, CI/CD, testing, and data quality practices. Manages senior data engineers and distributed teams, resolves complex engineering issues, governs technical delivery, and communicates strategies to executive stakeholders. Requires extensive enterprise data engineering, cloud platform, data warehousing, automation, and people leadership experience.

Principal Engineer responsible for architecting scalable, secure, near-real-time data pipelines and modernizing legacy systems onto GCP. The role develops data models, APIs, microservices, automation, and CI/CD workflows; supports AI and advanced analytics integrations; and advances data governance, lineage, compliance, access control, and quality monitoring. The engineer partners with product, analytics, and engineering teams to deliver data solutions for digital, retail, and operational use cases.

Develop technical enablement content, product demonstrations, solution guides, workshops, and training for HPE Apstra data center networking. Support sales, systems engineering, partners, and customers through presentations, technical expertise, and field enablement. Collaborate with engineering, product management, marketing, and regional teams on launches and positioning. Translate complex networking, automation, AI, and cloud infrastructure concepts into compelling technical narratives and customer value.

Build and operationalize scalable cloud data pipelines and infrastructure, primarily on GCP. Responsibilities include ETL/ELT development, complex data integration, data architecture, process automation, analytics tooling, performance optimization, CI/CD support, and collaboration with engineers, data scientists, product managers, and business stakeholders. The role focuses on healthcare and clinical data, including data platforms, operational systems, and infrastructure supporting business insights.

Designs and delivers scalable, low-latency streaming data pipelines using Apache Flink, Confluent Kafka, and Apache Iceberg. Modernizes batch and RDBMS workflows into event-driven architectures, builds lakehouse data structures, ensures data quality, optimizes streaming performance, and integrates ITSM and enterprise systems. The role also leads testing, documentation, delivery lifecycle activities, stakeholder collaboration, CI/CD, operational monitoring, and mentoring of data engineers.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Lead development of AI‑first analytics and data products, modernize the data mesh, build autonomous human‑in‑the‑loop agents for schema drift and governance, design scalable batch and real‑time pipelines, enforce lineage/DQ/governance, and provide technical leadership across analytics, platform, risk, and domain teams.

Lead design, build, and operate scalable data platform components and pipelines (batch and real-time) using Spark, Flink, Kafka, Airflow and Databricks. Implement data modeling, governance, cataloging, observability, and production-grade code; partner with stakeholders and lead technical evaluations and communities of practice.

Lead design, development, and production support of secure, cloud-native data and analytics systems. Drive adoption of AI-assisted engineering practices, improve automation and resiliency, evaluate vendor solutions, and mentor teams on secure, scalable software delivery.

Lead architecture and standards for CoreWeave's enterprise data ecosystem: design lakehouse/streamhouse architectures, modeling and semantic layers, governance, data quality, lineage, observability, and reusable data products. Drive cross-domain solutions, resolve performance and scalability constraints, evaluate core data technologies, conduct architecture reviews, and mentor senior engineers.

Develop and enhance Citi’s Regulatory Reporting Hub using Java, J2EE, Spring, big data technologies, and JavaScript. Responsibilities include translating business requirements into technical solutions, designing architecture, implementing data reconciliation processes, defining testing strategies, managing UAT, supporting production, improving code and processes, and mentoring junior engineers. The role also involves stakeholder coordination, project status reporting, and delivering reliable regulatory and financial reporting applications.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.

Leads enterprise data engineering and AI-ready data knowledge initiatives across Network Services. Designs scalable pipelines, data models, metadata, governance, lineage, quality controls, and validation frameworks for structured and unstructured data. Partners with subject matter experts to prepare trusted context for AI and agentic workflows, guides hybrid data architectures, supports testing and releases, establishes control standards, and mentors data engineers.

Lead Data Engineer responsible for building, optimizing, and operating secure, scalable data pipelines and platforms. Develop and run batch and streaming pipelines, workflow orchestration (Airflow), SQL-based transformations, and production-grade Python/PySpark code. Collaborate with analysts and data scientists, own system reliability and security, mentor junior engineers, and apply enterprise-authorized AI tools with strong validation and data sensitivity practices.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

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.

Design and build scalable, cloud-native backend services and AI-powered applications. Implement agentic AI solutions and integrate LLMs with enterprise systems. Develop REST/GraphQL APIs, microservices, and event-driven architectures. Deploy on AWS, containerize with Kubernetes/Docker, build CI/CD pipelines, and ensure observability. Collaborate with cross-functional teams to deliver secure, high-performance enterprise solutions and leverage AI-assisted developer tools.

Lead architecture and hands-on development of cloud-native AI web applications. Design end-to-end solutions integrating LLMs, vector search/RAG, real-time data, and scalable backend services. Implement model serving, RAG pipelines, AI agents, and ensure quality via testing and CI/CD while leading cross-functional engineering teams and translating business requirements into production-ready systems.

Design, build, and maintain scalable data pipelines and infrastructure on AWS, collaborating with data scientists and AI teams to support analytics and ML initiatives.

Lead ETL and data architecture for AML, trade/sales surveillance, and compliance data repositories. Design and develop complex Informatica PowerCenter 9.1 mappings, enforce ETL architecture standards, tune performance, automate and maintain ETL utilities, collaborate with BAs and DBAs, provide production support, and mentor junior ETL developers.