Data Engineer Jobs in Jersey City, NJ

Design, build, deploy, and operate production LLM-powered agents on JPMorganChase’s NEO platform. Responsibilities include Graph RAG, agent memory, context and entitlement management, multi-agent workflows, MCP integrations, evaluations, guardrails, observability, cloud deployment, security, resiliency, and collaboration with business and engineering stakeholders. The role also requires identifying data and architecture improvements across the software development lifecycle.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

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 global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Leads architecture and delivery of an enterprise-scale risk data platform using Databricks, AWS, Spark, and lakehouse technologies. Responsibilities include building ingestion and transformation pipelines, automating tenant onboarding, implementing infrastructure as code and CI/CD, embedding governance and security, improving reliability and observability, guiding architecture decisions, evaluating AI-assisted engineering practices, influencing senior stakeholders, and mentoring engineering teams.

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.

Lead the design, development, and operation of a business observability platform using React, Java, Spring Boot, Kafka, AWS, MySQL, and Databricks. Responsibilities include building event-driven microservices, data-heavy user interfaces, CI/CD pipelines, cloud deployments, production observability, and resilient data access patterns. The role also establishes engineering standards, leads cross-functional delivery, and promotes secure, responsible use of AI-assisted development tools.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

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.

Lead electrical engineering design and delivery for hyperscale data center facilities. Develop scalable electrical infrastructure solutions, review consultant and contractor designs, validate electrical loads and system performance, conduct technical studies, ensure code compliance, and support testing, inspections, commissioning, troubleshooting, budgets, and schedules. Coordinate with consultants, vendors, IT, operations, mechanical, and project teams. Travel to various data center sites is required.

Architects and deploys secure, scalable AWS cloud, data, and generative AI solutions for enterprise clients. Leads cloud migrations, legacy modernization, Infrastructure as Code, CI/CD, serverless and containerized implementations, and GenAI workloads using Bedrock and SageMaker. Acts as a client-facing technical leader, presents to executives, builds proofs of concept, identifies cloud expansion opportunities, and mentors engineers while developing reusable delivery accelerators and best practices.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Design and build cloud-agnostic Databricks Lakehouse architectures, scalable ETL/ELT pipelines, and production AI data workflows. Optimize Apache Spark performance, integrate MLflow for model deployment, and implement CI/CD and Infrastructure-as-Code using Terraform or Databricks CLI. Enforce governance, security, and compliance standards through Unity Catalog and cloud platforms. Collaborate with architects, scientists, product managers, and client engineering teams to deliver documented, production-ready solutions.

Designs, deploys, validates, and operationalizes AI/ML solutions for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making. Builds models, NLP capabilities, data pipelines, APIs, dashboards, and MLOps workflows across hybrid cloud and on-premises environments. Manages the model lifecycle, including governance, validation, monitoring, remediation, and production support. Partners with engineering, operations, risk, cybersecurity, architecture, data science, and product teams while communicating technical outcomes and risks to technical and executive stakeholders.

Designs, builds, and maintains scalable data pipelines and models across Microsoft Fabric. Responsibilities include ETL/ELT development, dimensional modeling, medallion architecture, performance optimization, data quality, governance, security, CI/CD deployment, and source-control management. The role partners with BI and business stakeholders to deliver reliable, properly structured data and owns work from estimation through production deployment.

Designs, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Python, Scala, Spark, and Azure data services. Responsibilities include optimizing distributed processing, managing data quality and validation, monitoring and troubleshooting pipelines, supporting production deployments, and collaborating with architects, analysts, and cross-functional teams. The role also requires Hadoop experience, Control-M workflow orchestration, SQL performance tuning, and adherence to testing, version control, and CI/CD practices.

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.

Supports actuarial data workflows by updating and testing SQL queries, comparing legacy and updated results, performing data quality checks, and documenting data sources and business rules. Partners with actuaries, data scientists, and other data professionals on process improvements while developing technical and communication skills through mentorship.