Data Engineer

• Design, develop, ingest, and maintain well-architected data pipelines that retrieve data from external feeds (APIs, SFTP, HTTPS, FTP, web scraping, Direct Connect) and internal agency sources into the data lake landing zone and downstream curated zones.
• Develop production-grade ETL workflows using AWS Glue, PySpark, Python, Lambda, and EMR, integrated with a shared ETL common library and orchestrated via Amazon Managed Workflows for Apache Airflow (MWAA).
• Load data accurately and optimally into S3 zones (Parquet, ORC, Iceberg), relational datastores (PostgreSQL, Redshift, Oracle), NoSQL databases, and knowledge bases/vector stores, preventing duplicate loads and maintaining data integrity and traceability across all lifecycle stages.
• Implement schema enforcement, XSD validation, data quality checks, error handling, and automated SNS notifications; ensure all production jobs populate ETL Load Reports and Gap Reports through static and dynamic ETL metadata.
• Develop semantic-layer objects (tables, views, materialized views) that ensure complete data coverage, optimized query performance, and consistent application of business logic.
• Develop XML parsing/shredding logic for high-volume regulatory filings using Glue PySpark, supporting schema evolution and batch processing per program standards.
• Design pipelines with query performance in mind and support rollback, reload, and date-range reprocessing capabilities without manual intervention.
• Support self-service ETL development by other agency teams through standardized, reusable components aligned with program standards, and facilitate the transition of externally developed ETL jobs into the Data Engineering team's production support.
• Create and maintain required engineering artifacts, including business requirements, ETL design documents, mapping documents, data models, data dictionaries, deployment references, operations and maintenance guides, and test plans.
• Deploy code through automated CI/CD pipelines using CloudFormation templates, following agency release, security, and governance processes.
• Provide operational support for production jobs, including rapid identification and resolution of failed jobs and performance issues; participate in on-call/after-hours support for production outages and emergencies as part of a team rotation.
• Collaborate with Data Officers, Data Stewards, SMEs, data providers, and IV&V teams to understand requirements and deliver user-accepted solutions; engage closely with the Product Owner and cross-functional teams to provide timely updates and resolve issues.
• Leverage AI-assisted development tools to accelerate coding, optimize workflows, and enhance code quality while adhering to security and performance standards.
• Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement, including participation in sprint planning and program increment planning.

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts.
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation.
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models.
• Develop and optimize Snowflake platform capabilities, including: Snowpipe, Streams, Tasks, File Formats, External Stages Dynamic Tables and ELT pipeline automation Warehouse sizing, performance tuning, and cost optimization.
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices.
• Translate complex business requirements into technical design specifications and actionable development plans.
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines.
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases.
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions. Implement robust monitoring frameworks using tools such as Snowflake Resource Monitors, CloudWatch, Datadog, or equivalent observability platforms.
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations.

Senior 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

• Design, build, and optimize scalable data pipelines supporting enterprise healthcare analytics and reporting use cases
• Lead automation efforts across ingestion, transformation, orchestration, and operational monitoring workflows
• Develop and maintain ingestion frameworks for structured and semi-structured healthcare data sources
• Mentor junior engineers and provide technical leadership across delivery teams

Lead Data Engineer

• Design and implement scalable data architectures supporting enterprise applications.
• Develop and maintain data ingestion, transformation, and processing pipelines.
• Engineer secure cloud-native data solutions within an IL5 environment.
• Develop backend services and APIs that support modern data platforms.
• Support data science initiatives through preparation, transformation, and validation of datasets.
• Generate synthetic, representative datasets using Government-provided schemas and statistical profiles when production data is unavailable.
• Collaborate with QA engineers to validate data quality, functionality, and system performance.
• Participate in software design, code reviews, testing, and technical documentation.
• Work directly with Government stakeholders and technical teams to gather requirements and deliver mission-focused solutions.
• Ensure compliance with security requirements, coding standards, and Agile development best practices.

Data Engineer / Data Architect – Mid to Senior, Top Secret Clearance Required

• 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

• This position is responsible for managing and organizing data to support all business processes to achieve corporate and departmental goals
• This position is responsible for identifying trends and communicating these trends clearly to others in the organization to ensure data is properly used
• Core activities include troubleshooting data issues, and assisting the data architect to develop, align, and maintain architectures with business requirements
• Develop data pipelines from various data sources to target locations — including MES, ERP, PLM, SCADA/historian, and quality systems — to support the enterprise Digital Thread, including formatting, cleaning, and updating data as the business needs
• Develop and maintain accurate data structure and mapping documentation, including metadata aligned to specific business requirements
• Design and publish domain-oriented, reusable data products aligned to a data mesh model with clear ownership, SLAs, and discoverability — standardizing data structure and types across ETL/ELT processes
• Understand the big picture, set the right scope, and collaborate with the data architect, application engineers, integration specialists, business analysts, and other technical experts to ensure adequate content delivery to authorized users in a timely, effective, and secure manner
• Manage the full life cycle development for the current ETL/ELT deployments, applying software-engineering practices to data pipelines — version control (Git), automated testing, code review, CI/CD, and Infrastructure-as-Code (e.g., Terraform, CloudFormation) — to ensure repeatable, auditable deployments across environments
• Partner with manufacturing operations, quality, and supply chain teams to deliver analytics on OEE, yield, scrap, throughput, engineering-change cycle time, time-to-release, and end-to-end product genealogy/traceability supported by the Digital Thread
• Prepare AI/ML-ready datasets — including feature pipelines, dataset versioning, and curated knowledge sources for predictive quality, anomaly detection, and retrieval-augmented (RAG) use cases — in partnership with data science and AI teams

• Serve as the go-to subject matter expert on Salesforce Agentforce for internal teams and enterprise customers.
• Advise definitively on optimal use cases, implementation patterns.
• Act as an escalation point for critical or complex Agentforce project challenges.
• Lead the technical design and hands-on development of complex Agentforce solutions.
• Partner with customers and account teams to assess business challenges, identify high-impact Agentforce use cases.
• Lead and contribute to mentorship and enablement programs.
• Maintain a high degree of expertise on the rapidly evolving Agentforce product roadmap.
• Provide critical “Voice of the Customer” feedback to Product and Engineering organizations.
• Actively partner with GTM counterparts during pre-sales activities.

Senior AI and Data Architect

• 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

• Design, construct, install, test, and maintain highly scalable data management systems and robust ELT/ETL pipelines across cloud and on-prem systems.
• Build infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using cloud technologies. Support API strategy, data modeling, and domain-driven data structures.
• Implement monitoring systems to ensure data integrity, quality, and security across all storage and processing layers. Implement data quality checks, validation, lineage tracking, and metadata management.
• Prepare and optimize data for AI/ML models, semantic search, analytics, and mission applications.
• Identify, design, and implement internal process improvements, such as automating manual processes and optimizing data delivery for greater scalability. Automate ingestion and transformation processes using industry-standard tools and patterns. Troubleshoot pipeline issues and optimize for performance and cost.
• Work with analytics and business teams to understand their data requirements and deliver production-ready datasets. Collaborate with AI engineers and analysts to ensure datasets meet requirements.

Hybrid Data Engineer

• The intent of this position is to provide our internal employees, 2026 Travelers Summer Interns and Summer Students the ability to apply to our 2027 Full-time or Intern opportunities.
• Innovative thinking, creative problem-solving, and a determination to challenge the status-quo, these are some of the defining qualities for participants of our Engineering Development Program (EDP).
• The EDP is a one-year program where you will be placed in one of the four designated Engineering tracks: Software, Data, Cybersecurity or Infrastructure & Cloud.
• Each track is designed to grow and develop your technical and self-leadership skills.
• Within your assigned track, you will receive a combination of hands-on opportunities, an immersive learning journey, and a mentoring program to prepare you for a dynamic career.
• EDP Participants in this track will partner closely with the business to refine features, plan user stories, and execute on the analysis, design, development, and testing of those user stories to deliver business capabilities to market.
• Additional program components include mentoring, networking, and career guidance.
• Please note that occasional travel for business meetings and events is an expectation of program participants.
• Perform other duties as assigned.

Engineering Development Program – Data Engineering

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• Define the enterprise data architecture supporting operational systems, analytical platforms, and AI initiatives.
• Establish the event-driven integration patterns, governance and metadata standards, and reusable data products.
• Assess legacy systems to understand data structures, data quality, storage mechanisms, and transformation logic.
• Work closely with the Systems Architect on enterprise direction, with Solutions Architects on product-level data needs, and with Data Engineers.
• Define the target-state data architecture spanning operational stores and the analytical data lake.
• Develop conceptual, logical, and physical data models that support both transactional systems and analytics.
• Design event-driven integration patterns that feed the EDL, using messaging and an enterprise service bus.
• Define reusable data products with clear ownership, documented schemas, lineage, quality expectations, and access policies.
• Define data governance, metadata, and cataloging standards.
• Ensure the data architecture supports analytics, machine learning, and Generative AI use cases.

Data Architect, Advisor

• 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

• Contribute to the design of cloud data warehouse architecture and the conformed customer data model
• Build and maintain SQL transformations, data models, and ELT/data pipelines that ingest, rationalize, and enrich customer data
• Leverage Merkle's common frameworks, reusable components, and AI-assisted development tools to accelerate delivery
• Apply development standards, version control, and engineering best practices
• Engineer customer features and assemble unified customer profiles for downstream CDP, CRM, analytics, and data science consumption
• Perform development, unit and data-quality testing, and peer code review
• Automate and orchestrate data pipelines, and manage code from development to production through source control and CI/CD
• Use AI coding assistants and GenAI tooling responsibly to improve productivity, code quality, and feature development

• Designs and develops moderately complex queries, objects, ETL packages, reporting structures, and other objects necessary to support detailed analysis and self-service reporting needs.
• Collaborates with analytics and business teams to create and improve data models that feed BI tools, increasing data accessibility, and fostering data-driven decision making across the organization.
• Takes ownership of the health, reliability, and security of the data platform by designing and implementing processes and solutions that proactively monitor data delivery across the full pipeline — ensuring data is accurate, complete, consistent, and timely from source ingestion through gold layer curation.
• Investigates, diagnoses, and resolves complex data pipeline failures across all system layers; uses findings to drive pipeline improvements and communicates status, root cause, and resolution clearly to the team throughout the full lifecycle of an incident.
• Assists with the development and delivery of data solutions, including data pipelines, API integration, and other structures and processes to support the data platform
• Identifies, designs, and implements internal process improvements, including the automation of manual processes, optimization of data delivery, and refactoring of code for improved performance
• Builds pipelines to enable the optimal extraction, transformation, and loading of data from a wide variety of data sources
• Provides strategic assistance to BI leadership and the BI team by making recommendations in the areas of data management and best practices
• Collaborates with developers, database administrators, data architects, and others on reporting and data management projects as needed
• Takes on other duties needed to help drive our Purpose, fulfill our Brand Principles, and abide by our Organization’s Values

Data Engineer II

• Collaborate closely with stakeholders, cross‑functional and internal technical teams to gather requirements, document business rules and develop thorough understanding of the business context and objectives
• Collaborate to design secure, scalable, and cost‑optimized data solutions
• Design, build, and maintain scalable, reliable ETL/ELT data pipelines using AWS, Snowflake and Snowflake tools
• Develop and optimize data models, both conceptual and physical, to support analytics, reporting, dashboards and operational consumption
• Implement data quality, validation, and monitoring frameworks to ensure accuracy and reliability
• Ensure data workflows are modular, testable, and properly version‑controlled
• Operationalize pipelines with monitoring, alerting, and automated recovery mechanisms
• Conduct advanced data analysis using languages such as Python and SQL
• Develop documentation to include data models, data dictionaries, and data usage guides
• Improve end-to-end performance of data workflows
• Build and maintain CI/CD pipelines using Jenkins to support automated testing, deployments, and continuous integration
• Meet schedule deadlines and commitments with a high-level of quality of deliverables
• Collaborate with a team of cross-functional resources in an Agile delivery environment to deliver iterative value

• Design, develop, and maintain scalable, cloud-based data pipelines using Python, PySpark, and SQL
• Process and standardize large-scale healthcare datasets
• Develop and maintain production databases and analytic datasets
• Perform data quality validation and consistency checks
• Collaborate with epidemiologists, statisticians, and analysts for data solutions
• Troubleshoot data pipeline failures and large-scale data processing issues

Data Engineer, Public Trust Required

• Partner with practice and delivery leadership to scale and mature our Data & AI capabilities
• Define and evolve our technical vision, reference architectures, and delivery methodologies
• Represent GOBI as a trusted advisor and subject matter expert in client executive conversations
• Build relationships with Microsoft and technology partners to stay ahead of platform evolution
• Lead solution architecture for strategic pursuits and complex proposals
• Conduct executive workshops, architecture reviews, and data strategy sessions
• Translate business challenges into compelling, defensible technical solutions
• Develop client-ready assets: architecture blueprints, roadmaps, and business cases
• Design enterprise-scale data platforms spanning ingestion, transformation, storage, analytics, and governance
• Architect solutions for both real-time streaming and batch processing workloads
• Define data governance frameworks leveraging Microsoft Purview and industry best practices
• Ensure solutions are secure, performant, cost-optimized, and production-ready
• Provide architectural governance across active engagements
• Conduct design reviews, resolve technical blockers, and ensure delivery quality
• Champion documentation standards, knowledge transfer, and long-term maintainability
• Mentor architects and engineers, raising the bar across the team
• Create high-quality technical content: blogs, white papers, reference architectures, and presentations
• Represent GOBI at industry events, webinars, and Microsoft partner forums
• Contribute to practice IP development and accelerator creation

Data Engineer jobs in Maryland

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Data Engineer

Senior Data Engineer

Director, Data Engineering – Business Data

Lead Data Engineer

Data Engineer / Data Architect – Mid to Senior, Top Secret Clearance Required

Senior Data Engineer

Senior Data Engineer

Senior AI and Data Architect

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Hybrid Data Engineer

Engineering Development Program – Data Engineering

Data Engineer

Data Architect, Advisor

Staff Data Engineer

Data Engineer

Data Engineer II

Senior Data Engineer

Data Engineer, Public Trust Required

Lead Data Architect