Data Engineer Jobs in Jersey City, NJ

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.

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 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 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.

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.

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.

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.

Architect, build, and operate secure, scalable data platform capabilities covering ingestion, transformation, orchestration, metadata, lineage, and data quality. Lead complex initiatives from architecture through production, establish reliability and engineering standards, automate deployment and onboarding, and drive operational excellence. Partner with security, risk, controls, and business teams on governance and regulatory requirements while mentoring engineers and influencing technical direction across teams.

Leads software engineering for data platform and entitlement solutions within an agile financial services technology team. Designs, develops, troubleshoots, and maintains secure, scalable Java and Spring applications using microservices, Kafka, cloud platforms, and CI/CD. Creates architecture artifacts, develops relational data models, improves system architecture, and promotes responsible AI-assisted engineering practices. Preferred experience includes modern data platforms, Spark, IAM, authorization models, and enterprise AI integration.

Build and maintain large-scale data pipelines and platforms using Python, PySpark, and AWS data services. Optimize data processing performance, troubleshoot technical issues, and support scalable data engineering solutions. Requires a bachelor’s degree in engineering, data science, IT, or a related field, along with 0–2 years of experience or recent graduation.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product 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.

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.

Senior Data Engineer responsible for designing scalable data pipelines and ELT/ETL workflows using PySpark, Snowflake, AWS, and SQL. The role supports insurance analytics, cloud migration and modernization, data warehouse modeling, data quality validation, performance optimization, and technical standards. The engineer will troubleshoot complex data systems, develop transformations and stored procedures, and document pipeline processes.

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 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 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.