Data Engineer

• Align data solutions to business requirements by planning and managing data infrastructure and strategy for our AI/ML Maintenance, Sustainment, and Deployment Planning Project.
• Enhance and optimize data entry, management and extraction within databases to ensure usability within proprietary systems.
• Perform data quality checks, analysis, present data and document processes.

Senior Data Engineer

• Design, build, and operate batch and streaming data pipelines on modern cloud data platforms.
• Develop robust ETL/ELT processes using SQL, Python, and PySpark.
• Implement layered / medallion data architectures and analytics-ready data models.
• Lead the implementation and evolution of data governance practices.
• Define and operationalize governance standards for data products and AI/ML features.
• Design and build AI agents and intelligent automation workflows.

Senior Data Engineer, Governance Focus

• Support the design and execution of comprehensive data migration strategies, including source-to-target mapping, transformation logic, data cleansing, and validation activities
• Perform data extraction, transformation, and load (ETL) processes from legacy applications into SAP S/4HANA using SAP migration tools and related technologies
• Collaborate with client stakeholders to gather migration requirements, facilitate workshops, and provide recommendations on data migration approaches and best practices
• Conduct data quality assessments and participate in remediation efforts to improve the accuracy, completeness, and consistency of migrated data
• Develop and maintain migration-related documentation, including mapping specifications, conversion procedures, validation results, and process workflows
• Monitor migration activities throughout project execution, identify and resolve data-related issues, and support defect management processes
• Assist with mock conversions, testing cycles, reconciliation efforts, and cutover planning to ensure successful migration outcomes
• Provide guidance and knowledge transfer to project teams and client personnel on data migration methodologies, governance, and leading practices
• Partner closely with cross-functional project teams to support implementation milestones and overall program success.

SAP Data Migration Consultant – Government Clearance Eligible

• Own day-to-day administration, configuration, and health of Oura's global Databricks environment, serving stakeholders across IT, business, and data science domains.
• Manage workspace governance: access controls, cluster policies, cost monitoring, job scheduling, and security configurations.
• Support onboarding of new teams and use cases onto the Databricks platform as Oura unifies data across Finance, SDM, Commerce, Marketing, and other domains.
• Proactively identify and resolve performance bottlenecks, reliability issues, and cost inefficiencies.
• Maintain SLAs for platform availability and incident response across global time zones.
• Partner with IT and Data Architecture teams to align platform configurations with Databricks best practices and Oura's Data Mesh approach.

Senior Data Engineer, Data Platform Admin

• This role supports the large-scale modernization of legacy systems by leading the migration of over 10TB of documentation and records into a cloud-based environment.
• The position focuses on transforming document-heavy workflows into structured, digital processes, while enabling seamless data integration across systems using MuleSoft APIs.
• The work directly supports enhanced analytics, reporting, and operational efficiency for a mission-critical enterprise platform.
• Lead end-to-end data migration from legacy systems to cloud platforms, including data mapping, transformation, and validation (Salesforce-centric).
• Analyze and convert unstructured, document-heavy data into structured, analytics-ready formats using Document AI.
• Implement data quality processes (cleansing, deduplication, reconciliation) to ensure accuracy and completeness post-migration.
• Support phased migration and system cutover with minimal operational disruption.
• Design, build, and maintain MuleSoft integrations, including API development for real-time and batch data exchange.
• Apply data transformation logic (e.g., DataWeave) and troubleshoot integration/data flow issues across systems.
• Contribute to Salesforce Data 360 implementation, including data harmonization, identity resolution, and unified profiles.
• Ensure proper data ingestion, lineage, metadata management, and traceability across the ecosystem.
• Ensure compliance with data governance, privacy, and security requirements (e.g., PII).
• Collaborate in Agile teams; support testing, documentation, and audit readiness across migration and integration efforts.

• Own the design and organization of the program’s data lake, including schema definitions, partitioning strategy and metadata indexing.
• Design and maintain end-to-end pipelines that ingest high-bandwidth sensor logs from vehicles into cloud storage with high reliability and tolerant of ad-hoc and intermittent connectivity mechanisms.
• Develop data validation and integrity checks that can detect corrupted information, missing sensors, and inconsistent calibration prior to the data being processed by downstream systems.
• Implement retention, tiering and lifecycle policies for data to balance storage costs with development value.
• Build tooling to query raw logs to produce curated training and evaluation datasets.
• Build automation to run cost-effective pseudo-labeling workflows at the scale of data ingest.
• Implement data quality and model performance metrics that are used to direct labeling effort toward the highest-value examples.
• Deploy and maintain data visualization tooling to support log review, annotation QA, and autonomy debugging workflows.
• Build integrations between the visualization tooling and the data lake so engineers can navigate from a dataset entry or model failure directly to the origin log data
• Work with autonomy engineers to define and surface custom visualization panels and implement metrics for analyzing unstructured operating environments.
• Build dashboards that provide the autonomy engineers visibility into data coverage by terrain type, operating environment and geographic region.
• Establish and document data contracts between the data services and model training consumers.
• Partner with perception, planning and embedded engineers across the data lifecycle: from shaping the logging schemas and collection triggers to defining the dataset interfaces that supply model training and evaluation.
• Define data engineering standards, best practices, and tooling choices for an innovative and fast-paced team.
• Contribute to the data roadmap and provide input to technical leadership on investment priorities.
• Mentor junior engineers and raise the team’s capabilities in data infrastructure scalability and operational hygiene.

Senior Autonomy Data Engineer

• Own and manage day-to-day operations of Data & Analytics platforms, ensuring SLA-driven support and service delivery
• Support cross-functional teams by delivering data insights for complex business problems
• Collaborate with data engineers, data scientists, and stakeholders to deliver data-driven outcomes
• Gather requirements, define data models, and support solution design with architects and engineering teams
• Perform data exploration, analysis, and maintain data definitions, business rules, and lineage
• Drive documentation, testing (functional, regression, exploratory), and execution of test plans
• Build, enhance, and support scalable data pipelines (batch and real-time) across multiple systems
• Implement data governance practices and promote data literacy across the organization
• Identify and address technical debt; continuously improve and optimize processes
• Monitor, troubleshoot, and tune performance of data platforms and analytical solutions
• Champion automation-first, enterprise-first, and reusable solution design principles
• Support infrastructure and cloud-based solutions including SaaS, PaaS, IaaS, and IaC practices
• Communicate insights and results through reporting and presentations

Lead Data Engineer

• Develop and maintain structured data models supporting sustainment analytics and dashboard requirements
• Build and maintain automated ETL workflows using AWS GovCloud services (S3, Glue, Lambda, Athena)
• Apply IL5 cloud controls, NIST 800-53 security requirements, and Zero Trust principles
• Integrate REST APIs to ingest data from multiple government systems
• Apply unit testing and validation techniques to ensure data quality

F-35 Data Engineer, Mid

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Apply engineering principles and methodologies to solve complex technical challenges
• Troubleshoot technical issues and develop effective solutions in a timely manner
• Stay up to date with the latest advancements in relevant technologies
• Collect, analyze, and interpret large datasets using Excel and other analytical tools
• Develop and maintain insightful reports and dashboards to track key performance indicators (KPIs) and identify trends
• Use data-driven insights to make recommendations for process improvements, cost reduction, and efficiency gains
• Communicate technical information clearly and effectively to both technical and non-technical audiences
• Collaborate with cross-functional teams (e.g., sales, marketing, operations) to achieve common goals.
• Present technical findings and recommendations to management in a concise and persuasive manner
• Act as a technical resource and mentor to junior engineers
• Understand the business implications of engineering decisions and contribute to strategic planning
• Identify opportunities to leverage technology to improve business processes and gain a competitive advantage
• Participate in cost-benefit analyses and ROI calculations for engineering projects.
• Stay informed about industry trends and market dynamics
• Develop project plans, track progress, and identify and mitigate risks
• Coordinate resources and communicate effectively with stakeholders

Data Architect, Information Strategy

• Collaborate with Agile teams to design, develop, test, implement, and support technical solutions in Big Data development tools
• Work with a team of developers with deep experience in machine learning, distributed microservices, and ETL systems
• Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake
• Share passion for staying on top of tech trends, experimenting with and learning new technologies, mentoring others
• Deliver robust cloud-based solutions that drive powerful experiences for customers
• Perform unit tests and conduct reviews with other team members

Lead Data Engineer – Intelligent Foundations and Experiences

• Develop and maintain enterprise data and analytics strategy documents, roadmaps, and improvement plans (including POA&Ms) for DLA J6TF
• Design and support an enterprise data governance framework — governance structure documentation, data ownership assignments, data stewardship roles, and operating procedures
• Manage metadata per DoD standards, including DoD Metadata Registry (DoD 8320 series) and Net-centric Enterprise Services requirements
• Provide architecture recommendations for DLA analytical platforms — data ingestion, storage, processing, and delivery layers
• Support enhancement of DLA's Enterprise Data Warehouse (EDW) — logical and physical schema design, data source integration, and analytical layer development
• Develop data dictionaries, data inventories, and data lineage documentation across DLA's multi-system enterprise environment
• Identify and register authoritative data sources across DLA data domains; document authoritative source designations per governance policy
• Conduct data quality assessments — profile source data, document quality issues, and produce remediation recommendations
• Support data governance working groups and data stewardship council activities — facilitation, briefings, and deliverable preparation
• Develop and maintain artifacts including logical data models, conceptual data models, metadata management plans, and enterprise architecture alignment documents
• Prepare monthly progress reports, IPR briefing charts, and deliverables for COR review

Senior Data Architect

• Design, build, and maintain scalable batch and near-real-time data pipelines using cloud-native services
• Develop and optimize data ingestion, transformation, and orchestration workflows across diverse data sources
• Build and maintain ELT/ETL frameworks to support analytics, reporting, and data science use cases
• Prepare, transform, and curate raw data into analytics-ready datasets for both technical and non-technical stakeholders
• Develop, deploy, and operate data products within Azure-based analytics platforms (e.g., Databricks, Synapse, Fabric)
• Implement data quality checks, monitoring, and observability to ensure data accuracy, reliability, and integrity
• Apply data governance, security, and privacy controls aligned with enterprise and regulatory standards
• Monitor data platform performance and proactively implement cost and performance optimizations
• Partner with data scientists, analysts, and analytics engineers to ensure trusted and timely access to data
• Design data solutions that are scalable, reusable, automated, and well-governed by default

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
• Work with a team of developers with deep experience in machine learning, distributed microservices, and full stack systems
• Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community
• Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
• Perform unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance

• Design, build, and maintain saleable data pipelines within Snowflake from multiple sources
• Build and maintain medallion architecture (bronze/silver/gold layers) for data lake solutions
• Design data models that support both businesses intelligence and AI requirements
• Lead cloud-native data platform evolution, integrating Snowflake with AWS.
• Collaborate with business analysts, data scientists, and stakeholders to understand data requirements and translate them into technical specifications.
• Implement data quality frameworks and monitoring systems to ensure accuracy, completeness, and consistency of data assets.
• Develop and maintain comprehensive documentation for data pipelines, models, and processes.
• Optimize query performance and resource utilization to reduce costs and improve processing times.
• Participate in on-call rotation to support production data systems and resolve incidents.

• Manager II will oversee and lead data engineering initiatives within the Enterprise Data Governance program.
• This program’s objective is to enable a unified and high-quality data environment in the Azure Cloud that continuously innovates to enhance decision-making for members and employees.
• Key responsibilities include maturing the Data Catalog solution, advancing Data Quality capabilities, and streamlining Data Access Management.
• This role requires proven people-management experience, including leading direct reports and driving employee engagement.
• The role is responsible for budget planning and management, as well as building strong relationships with diverse business stakeholders through a confident leadership presence to deliver large-scale data initiatives.
• The role also promotes the culture of the Data Center of Excellence (CoE), and delivers AI-driven, scalable data governance solutions.

Manager II, Data Engineering – Enterprise Data Governance

• responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem across two independent health plan technology stacks.
• owns the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization), and improves the reliability, observability, and security posture of data pipelines supporting critical Medicaid operations.
• partners closely with the Market BI team as their IT counterpart to improve data access and flow, including establishing and managing Databricks pipeline patterns and platform enablement as the BI environment evolves.
• accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex dual-stack environment.
• primary IT partner for data platform enablement — improving data access patterns, strengthening pipeline governance, and helping establish a maintainable approach to Databricks pipelines and workflows as the market matures its analytics capabilities.

Lead Data Engineer – Modernization, Reliability

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
• Lead a team of developers with deep experience in machine learning, distributed microservices, and full stack systems
• Utilize programming languages like Python and Java and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community
• Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
• Perform unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance

Senior Lead Data Engineer – Bank Tech

• Help clients find answers in their data to impact important missions
• Develop and deploy the pipelines and platforms that organize and make disparate data meaningful
• Work with a multi-disciplinary team of analysts, data engineers, developers, and data consumers
• Support the assessment, design, developing, and maintenance of scalable platforms

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

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