Data Engineer Jobs in Jersey City, NJ

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

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.

Lead design, develop, and maintain secure, scalable data and AI-driven systems. Build data pipelines on AWS/Databricks, write production code, drive AI-assisted engineering adoption, enforce validation/security standards, and mentor teams on responsible AI practices.

Leads end-to-end delivery of software and data solutions, including Java-based microservices, full-stack applications, cloud-native AWS services, data architectures, AI/ML models, KPIs, and analytical reporting. Provides technical leadership across application, platform, and data teams; establishes roadmaps and governance; improves reliability through observability and SRE practices; and drives CI/CD automation, legacy modernization, and responsible AI-assisted engineering practices.

Leads architecture and hands-on development of scalable ML and generative AI platforms. Builds Python APIs and SDKs, Databricks data and ML workflows, agentic AI capabilities, model integrations, infrastructure automation, and CI/CD pipelines. Ensures reliability, monitoring, security, performance, and cost efficiency while mentoring engineers, partnering with data science and product teams, and establishing AI-assisted engineering quality standards.

Leads software engineering efforts for a data platform within an agile corporate technology team. Designs, develops, tests, debugs, and maintains secure, scalable production systems; reviews code; improves operational stability through automation; and promotes responsible AI-assisted development practices. Evaluates vendors and architectural solutions, coaches engineers on secure AI use, and applies cloud-native technologies, software development lifecycle practices, resiliency, and security standards in financial services systems.

Lead software engineering and reliability efforts for a data platform within an agile Corporate Technology team. Responsibilities include designing, developing, testing, troubleshooting, and reviewing secure production code; improving application resiliency and automating recurring remediation; promoting validated AI-assisted engineering practices; applying SDLC automation; and evaluating vendor and architectural solutions. The role requires strong software engineering, cloud-native, security, CI/CD, agile, operational stability, and financial services technology expertise.

Leads development of secure, scalable data and AI platform solutions, including batch and real-time pipelines, ETL/ELT workflows, data modeling, governance, cataloging, lineage, quality frameworks, and semantic layers. Builds Agentic Autonomous Lakehouse capabilities for governed self-service provisioning and operational optimization. Partners with technical and business stakeholders, reviews production code, improves system stability, evaluates architectures and vendors, and promotes emerging technologies across engineering communities.

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.

Lead software engineering for JPMorganChase’s Corporate Data and Analytics Services team. Design, develop, test, troubleshoot, secure, and maintain scalable production systems using Java, Python, AWS, and agile SDLC practices. Review code, improve resiliency and automation, remediate recurring issues, and promote responsible AI-assisted engineering practices. Lead architectural evaluation sessions with vendors and internal teams while coaching engineers on secure, compliant AI tool adoption.

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.

Leads the design and development of secure, scalable software frameworks and production systems. Builds reusable engineering platforms, reviews code, improves SDLC automation, and governs agentic AI-enabled development workflows with responsible AI, security, resiliency, auditability, and human-in-the-loop controls. Advises cross-functional teams, solves complex technical problems, promotes cloud-native practices, and influences senior technology and business leaders.

Lead design and implement scalable hybrid cloud solutions for transactional, analytics, and RAD workloads. Write and review code, drive AI-assisted engineering adoption, apply SDLC tools, collaborate with leads and stakeholders, conduct code reviews, present technical updates, prioritize work, and mentor team members.

Lead design and delivery of reliability, observability, and SRE practices for large-scale data and AI/ML platforms. Define NFRs, SLI/SLOs, incident response, and implement reliable, secure, scalable infrastructure and automation while mentoring teams and driving safe, auditable AI-assisted operations.

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 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 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.

Lead and deliver secure, high-quality software solutions for financial planning and analysis. Design, develop, review, and troubleshoot production code; drive adoption of enterprise AI-assisted engineering practices; leverage AWS and big-data technologies like Spark; evaluate vendors; automate remediation to improve operational stability.

Partner with sales teams to design and present data security solutions, gather technical requirements, lead hands-on evaluations and custom configurations, design evaluation test plans, and guide prospects to successful outcomes.

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.

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, build, deploy, and maintain production-grade ML/data platforms and end-to-end model pipelines. Engineer large-scale data ingestion, transformation, storage (data lakes/warehouses), monitoring, CI/CD, and production reliability. Resolve production incidents quickly, add features, and drive adoption of enterprise AI-assisted engineering practices while ensuring responsible AI, security, and validation standards across the team.

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.