Data Engineer

• Lead and grow a team of data engineers, providing mentorship and leadership within the function
• Build the future of the data engineering practice to deliver value to internal and external stakeholders while leveraging cutting edge technologies to dramatically accelerate quality, scale, and depth of delivery
• Design and implement scalable data pipelines to collect, process, and store large volumes of critical data from various sources
• Drive the evolution of the Business Data platform by leveraging modern data technologies like Spark, Airflow, and Databricks
• Collaborate across organizational boundaries with Product, Marketing, and Sales to convert business needs into technical requirements
• Authoritatively translate business use cases into well-thought-out data models that evolve with the business

Director, Data Engineering – Business Data

• Contribute to the design and continuous improvement of the enterprise DLP architecture across network, endpoint, email, and cloud channels
• Deploy, configure, tune, and document DLP solutions across network egress points (web proxy, email gateway, API channels) and endpoint agents at enterprise scale
• Support the development of DLP policies aligned with data classification frameworks and labeling taxonomies across all data channels
• Conduct regular policy effectiveness reviews and false-positive tuning sessions
• Engineer end-to-end event monitoring pipelines, integrating DLP alert telemetry into SIEM platforms for correlation, enrichment, and dashboarding
• Develop automation workflows and playbooks to accelerate DLP incident triage, notification, and response.

Senior Data Loss Prevention (DLP) Architect, Engineer

• Oversee the design, development, testing, deployment, and management of enterprise technology AI products and IMSC integrated data platform.
• Lead work across multiple Technology Product and Platform Management families, with accountability for AI end‑user applications, and AI‑enabled platforms supporting the IMSC Transcend program.
• Ensure alignment between business objectives, AI‑ready data maturity enabled by SAP BDC and AI capabilities enabled by SAP Joule, and scalable technology solutions integrated with broader IMSC Data and AI platforms (Aspire.ai).
• Drive measurable business outcomes through disciplined software engineering, platform governance, and innovation.
• Define platform strategy, architecture decisions, and build/buy/blend approaches in partnership with enterprise architecture and business AI leadership.

Senior Manager – IMT Large Program, AI, Data Platforms

• Leads the architecture, engineering, and ongoing operation of the security data lake and associated data products supporting multiple security technologies and data sources
• Establishes the technical roadmap, engineering standards, reusable design patterns, and platform operating model
• Acts as a subject matter expert among peers, with manager and senior management on all matters pertaining to data platforms and the supporting architecture
• Develop and maintain the end-to-end architecture of the security data lake, including ingestion, storage, transformation, enrichment, semantic, and consumption layers
• Ensure data pipelines comply with security policies and regulatory requirements
• Develop testing plans to ensure quality of implementation
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues
• Maintain relationship with key vendors
• Create presentations and seek IT management approval and acceptance of significant replacements or reconfigurations of the data pipeline

Senior Data Engineer

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• The Principal Translational Knowledge Architect & Graph Lead will be responsible for designing and implementing the semantic and knowledge architecture that enables AI-driven reasoning across the drug discovery and development lifecycle.
• This role will serve as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation.
• Working at the intersection of translational science, patient safety, biomedical informatics, and artificial intelligence, this individual will help establish the semantic foundation required to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework.
• The successful candidate will partner closely with scientists, safety experts, data scientists, AI engineers, and platform teams to create knowledge assets that support GraphRAG, agentic AI, scientific reasoning, and next-generation translational intelligence capabilities.

Principal Scientist, Data Science – Translational Knowledge Engineering

• You will own and evolve our data pipelines on GCP — building new ones, hardening existing ones, improving data quality, and making clean, trustworthy data available across the organisation.
• Designing ELT/ETL processes on BigQuery and ClickHouse, building real-time pipelines on Pub/Sub and Kafka with Dataflow (and where it fits, Flink/Spark).
• Orchestrating workflows with Airflow, and ensuring data is properly cleaned, modelled, and served for analytics, ML training, and online inference.
• Partnering with ML engineers on feature pipelines, monitoring data drift, and keeping models well-fed and retrained as needed.
• Consuming and building REST APIs, integrate with third-party SaaS sources, and treating infrastructure as code.
• Collaborating closely with Infrastructure, Software Engineering, Product, and ML/AI engineers.
• Helping drive the migration off Snowflake onto GCP-native stack — and retire shadow pipelines along the way.
• Owning the orchestration layer in Airflow, including SLAs, retries, and data quality gates.
• Modeling data for analytics and for ML — including feature pipelines that serve both training and low-latency online inference.
• Capturing requirements from stakeholders and translating them into pragmatic, well-scoped data products.
• Continuously improving data quality, reliability, observability, and cost efficiency.
• Identifying new data sources worth acquiring and integrating them cleanly.

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
• Design, build, and operate batch, micro-batch, and streaming pipelines feeding Snowflake — Prefect-orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
• Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version-controlled documentation.
• Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no ClickOps in production.
• Build AWS-native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
• Maintain CI/CD pipelines (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review.
• Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; right-size before scaling up.
• Design RBAC, masking policies, and row-access policies that satisfy a regulated operator without becoming an access bottleneck.
• Own freshness SLAs and data contracts for the gold layer; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end-to-end.
• Collaborate with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

• Finds trends in datasets and develops workflows and algorithms to make raw data useful to the enterprise
• Designs and implements standardized data management procedures around data staging, data ingestion, data preparation, data provisioning, and data destruction (scripts, programs, automation, assisted by automation, etc.)
• Ensures quality of technical solutions as data moves across Healthfirst environments
• Provides insight into the changing data environment, data processing, data storage and utilization requirements for the company and offers suggestions for solutions
• Ensures managed analytic assets support Healthfirst’s strategic goals by creating and verifying data acquisition requirements and strategy
• Develops, constructs, tests and maintains architectures
• Aligns architecture with business requirements and uses programming language and tools
• Identifies ways to improve data reliability, efficiency, and quality
• Conducts research for industry and business questions
• Deploys sophisticated analytics programs, machine learning and statistical methods
• Prepares data for predictive and prescriptive modeling and finds hidden patterns using data
• Uses data to discover tasks that can be automated
• Creates data monitoring capabilities for each business process and works with data consumers on updates
• Aligns data architecture to Healthfirst solution architecture; contributes to overall solution architecture
• Helps maintain the integrity and security of the company data

Healthcare Claims, Data Engineer

• Define the target architecture for the enterprise data context self-service platform.
• Establish architectural standards and patterns for: context publishing discovery access control versioning validation lineage and provenance observability and monitoring
• Partner with product, engineering, service design, security, and governance teams to ensure the platform is technically sound and aligned to enterprise needs.
• Evaluate and recommend technologies and integration patterns including: ontology and semantic modeling approaches OWL/RDF-based systems knowledge graphs semantic layers MCP, A2A, and Timbr-like technologies where applicable
• Design architecture that supports both self-service user workflows and AI/agentic consumption patterns.
• Ensure context can move from development to production in a governed, scalable, and supportable way.
• Define non-functional requirements including performance, resilience, security, compliance, and operational maintainability.
• Review solution designs and implementation plans to ensure adherence to architectural standards.
• Create reference architectures, patterns, and decision records to guide implementation teams.
• Collaborate with platform teams to support long-term scalability, reuse, and interoperability across domains.
• Help shape how the platform supports agentic AI workflows, including structured prompt context, tool access, and reliable data grounding.

Lead Architect, Data Context Layer

• Design, build, and maintain efficient, reusable, and reliable architecture and code for data pipelines and data applications on AWS.
• Build robust data ingestion pipelines (from on-prem to AWS and within AWS) using AWS services such as Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, and SQS.
• Develop and manage ETL/ELT processes to collect, process, and store data from multiple sources, ensuring data quality, integrity, and security.
• Architect and implement end-to-end data solutions (ingestion, storage, integration, processing, access) on AWS, with a focus on data lakes and data warehouses.
• Participate in the architecture and system design discussions for high-scale data engineering projects.
• Independently perform hands-on development, unit testing, and participate in code reviews to ensure adherence to best practices.
• Implement serverless applications using AWS Lambda, API Gateway, Step Functions, and other AWS technologies.
• Migrate data from traditional relational databases, file systems, and APIs to AWS-based data lakes (S3), RDS, Aurora, and Redshift.
• Implement high-velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred).
• Architect and implement CI/CD strategies for enterprise data platforms.
• Collaborate with product, operations, QA, and cross-functional teams throughout the software development cycle.
• Stay abreast of new technology developments, implement POCs for new tools/technologies, and onboard them for real-world use cases.
• Identify and resolve performance issues and continuously optimize for cost, reliability, and scalability.

Lead Data Engineer

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse.
• Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly.
• Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage.
• Use dbt to transform raw data within the data warehouse into structured, production-ready schemas.
• Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices.
• Perform rigorous data quality checks by implementing native dbt tests.
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds.
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability.
• Build data applications that interact with the data warehouse to empower decentralized self-service analytics.
• Build internal tooling and libraries to facilitate analytics and ML work.

Staff Data Engineer

• Analyze integration and system requirements by understanding business needs and designing effective data solutions, particularly for Guidewire Policy, Billing Center and Commercial P&C data domains.
• Design, develop, and optimize ELT pipelines to ingest, transform, and load data into a Delta Lakehouse platform.
• Design, develop and maintain data models and schemas ensuring data quality and integrity.
• Build and maintain dashboards and reports delivering actionable business insights.
• Monitor pipeline and storage performance; troubleshoot and resolve data issues promptly.
• Collaborate with cross-functional teams, including analysts and business users, to deliver end-to-end insurance data solutions.
• Implement data governance, security, and compliance standards across platforms.
• Conduct root cause analysis for system failures and performance events; drive continuous improvements in enterprise data integration pipelines.
• Create and manage testing procedures (unit, scenario, end-to-end) to ensure pipeline reliability.
• Stay current with emerging technologies and recommend improvements to workflows.
• Mentor team members on data engineering tools, Guidewire data model concepts and best practices.
• Build and maintain data pipelines to process structured and unstructured data (like documents and text) for Generative AI tasks, including creating embeddings and working with vector databases to support AI search features.
• Prepare and clean large datasets to ensure high-quality inputs for training and fine-tuning Generative AI models.
• Collaborate with AI and data science teams to understand data requirements and deliver scalable solutions that support model training and inference.
• Implement processes to enrich data with metadata and context to improve AI model accuracy and relevance.
• Optimize data storage and retrieval methods to support fast, low-latency responses for AI-powered applications.
• Monitor data workflows for Generative AI projects, troubleshooting issues, and ensure continuous pipeline performance.

Data Engineer 2 – Guidewire

• Design and implement data models across relational, graph, and lakehouse systems using AWS RDS, AWS Neptune, and Databricks
• Develop warehouse/lakehouse schemas in Databricks Delta Lake for analytics and ML
• Implement graph-based retrieval and integrate with vector search for AI use cases
• Generate embeddings and manage hybrid retrieval pipelines (graph + vector)
• Build ETL/ELT workflows using Databricks (PySpark), Airflow, or AWS Glue
• Ensure data quality, consistency, and freshness across systems
• Tune queries and optimize storage for Neptune, RDS, and Delta Lake
• Work with data scientists and AI engineers to support knowledge graph and RAG workflows
• Document data models and maintain governance standards

IAM IGA Data Engineer, Assistant Vice President

• Lead strategic MDM integration initiatives - Architect and oversee seamless, high-volume data integration between Databricks lakehouse and NeoXam MDM platform, establishing foundational data architecture for multi-billion-dollar index-linked investment products
• Drive advanced cloud data architecture - Utilize cutting-edge Databricks features including Unity Catalog, Delta Live Tables, and Serverless compute to optimize data pipelines and maximize performance for massive-scale financial datasets
• Pioneer AI/ML automation solutions - Implement Databricks Mosaic AI and agentic frameworks to automate data transformation, quality assurance, and anomaly detection across the entire conversion lifecycle
• Build and scale high-performing engineering teams - Lead, manage, and grow multiple teams of product-focused software and data engineers while mentoring their professional and technical development
• Modernize legacy infrastructure - Spearhead mission-critical migration of legacy assets and terabytes of data to modern AWS cloud ecosystem, breaking monolithic applications into microservice architectures
• Champion engineering excellence and innovation - Foster a culture of cutting-edge technology adoption, continuous improvement of software engineering practices, and delivery of scalable, resilient solutions

Senior Director, Software Engineering – Data Platform, MDM Integration

• Lead the product team through opportunity validation, justification, product definition
• Support business development activities
• Investigate new market opportunities
• Engage U.S. and global defense customers

Principal Engineer, Product Marketing – High Speed Data Converter, Transceiver

• Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets.
• Define the technical vision for DC data architecture, mentor engineers, and manage external contractors to ensure the team delivers high-quality, practical solutions for current and future needs.
• Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.

Staff Software Engineer – DC Tech Lead

• Design, build, and maintain robust data pipelines and data lake architecture for both batch and real-time streaming use cases, including high-volume, low-latency data processing
• Improve observability, alerting, and SLOs across data systems so pipelines are easier to monitor and issues are caught early
• Optimize ETL/ELT workflows for performance, scalability, and fault tolerance
• Develop dbt workflows to onboard Evaluation Partners and create end-of-day reporting for partner analysis
• Build and support event-driven architectures and scalable platform components
• Contribute to the orchestration and automation of workflows
• Integrate complex financial APIs and third-party data sources into internal systems
• Collaborate with analytics, product, and ML engineers to develop and deploy reliable data products
• Work on feature pipelines and model-ready data to support ML engineers
• Promote high standards in code quality, testing, and platform reliability
• Participate in Agile ceremonies and foster a collaborative, growth-oriented team culture

Data Engineer jobs in New Jersey

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Director, Data Engineering – Business Data

Senior Data Loss Prevention (DLP) Architect, Engineer

Senior Manager – IMT Large Program, AI, Data Platforms

Senior Data Engineer

Senior Data Engineer

Principal Scientist, Data Science – Translational Knowledge Engineering

Senior Data Engineer

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Healthcare Claims, Data Engineer

Lead Architect, Data Context Layer

Lead Data Engineer

Staff Data Engineer

Data Engineer 2 – Guidewire

IAM IGA Data Engineer, Assistant Vice President

Senior Director, Software Engineering – Data Platform, MDM Integration

Principal Engineer, Product Marketing – High Speed Data Converter, Transceiver

Staff Software Engineer – DC Tech Lead

Senior Data Engineer