Data Engineer

• Design, develop, and maintain scalable data ingestion, transformation, and publishing pipelines utilizing Databricks and AWS services.
• Implement and optimize Databricks Lakehouse capabilities including Unity Catalog, Delta Live Tables, Auto Loader, Databricks SQL, and Delta Sharing.
• Build and maintain governed data products supporting operational, analytical, reporting, and machine learning workloads.
• Develop and support medallion architecture data pipelines and enterprise data quality frameworks.
• Implement data governance controls, metadata management, lineage tracking, and data retention policies.
• Collaborate with cloud engineers, architects, cybersecurity specialists, and business stakeholders to deliver secure, production-ready solutions.
• Optimize platform performance through partitioning, clustering, caching, workload tuning, and query optimization techniques.
• Support analytics enablement through semantic layers, dashboards, reporting solutions, and self-service data access capabilities.
• Participate in architecture reviews, operational readiness activities, platform modernization initiatives, and continuous improvement efforts.
• Create and maintain technical documentation, design artifacts, operational procedures, and engineering standards.

Senior Data Engineer – Databricks, AWS

• Designing, developing, and maintaining scalable data pipelines that ingest, reconcile, and validate Medicaid program data from MESH and connected CMS systems including T-MSIS, MBES, MacFin, SPoTT, and CMCS DataConnect
• Building and operating data ingestion workflows that support mixed-modality content — spreadsheets (Excel, CSV), documents (PDF, Word), API feeds, and enterprise data services — with full lineage and audit traceability
• Implementing the data layer that powers the Master Detailed Budget Table (MDBT) modernization, including parsing legacy and modern MDBT formats, normalizing line items (e.g., MMIS 2A/B, 4A/B, 5A/B/C), and validating total computable, state share, and federal share calculations
• Developing API services to facilitate bidirectional data integration between MESH (Salesforce) and external systems, including standardized integrations with T-MSIS for compliance and Data Quality Assessment status
• Building data processing workflows on AWS (S3, Glue, EMR, Athena, RDS/Redshift) and, where applicable, Databricks, Spark, and Hive for big-data processing
• Creating data products that feed Power BI dashboards and Salesforce reports, ensuring consistent definitions, refresh schedules, snapshot management, and data-quality indicators
• Implementing automated data quality, validation, and reconciliation jobs that flag funding decreases, mismatched FFP rates, missing submissions, and other anomalies
• Supporting AI/ML pipelines for the MESH platform — including features for AI-assisted submission analysis, predictive analytics, and anomaly detection — by providing curated, governed training and inference data
• Writing unit and integration tests for all data processing code and partnering with DevOps engineers on CI, CD, and IaC (GitHub Actions, Terraform) so pipelines deploy reliably
• Performing code reviews, contributing to data-engineering standards, and mentoring less experienced engineers on pipeline design, performance optimization, and observability
• Maintaining data security and privacy controls for data at rest and in transit (FIPS 140 validated encryption, KMS-managed keys, least-privilege access) consistent with CMS ARS 5.1 and FedRAMP Moderate baselines
• Documenting data dictionaries, lineage, calculation logic, and pipeline runbooks in CMS-approved tools (Confluence, GitHub) to support transparency, audit, and contract transition activities

Senior Data Engineer

• 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

• Design, implement, and maintain data pipelines using AWS services
• Collaborate with cross-functional teams to optimize data processes and analytics
• Analyze and optimize database performance and query efficiency
• Create documentation and provide support for data-related issues

• Assist in building and maintaining data pipelines
• Write and optimize SQL queries
• Support data transformation and validation tasks
• Work with structured datasets related to Congress, legislation, hearings, and lobbying data
• Support the implementation and maintenance of AI workflows for data engineering
• Collaborate with the team to improve data quality and documentation

Data Engineering Intern – Congressional Data

• Lead the design and stewardship of BSI's data governance architecture, data traceability standards, and quality frameworks, alongside oversight of reference and shared data management — with heightened emphasis on data integrity as a critical enabler of AI/ML model training and regulatory reporting.
• Establish and maintain enterprise standards for data lineage, data quality measurement, and governance processes, developed in close partnership with data governance stakeholders across the organization.
• Responsible for architecting reference and shared data patterns that underpin all business domains — Management Liability, Surety, and Claims — alongside the Analytical domain, ensuring a unified and governed approach to shared data assets across the enterprise.
• Collaborate with Enterprise teams to establish guidelines for AI-ready data, while identifying gaps and defining a clear roadmap to advance BSI's overall data readiness.
• Collaborate closely with the Data Engineering Value Stream to drive data governance implementation and engage with the Enterprise teams to align on and advance cross-enterprise data standards.
• Ensure data architecture decisions support and align with regulatory compliance requirements.
• Partner with senior leaders across Technology and Business to define architecture priorities, align on strategic direction, and ensure architectural decisions are firmly grounded in business imperatives.
• Collaborate with Enterprise architecture and center of excellence teams to define and advance architectural patterns aligned with modern principles; producing best practices, reference materials, and instructional videos that drive consistent adoption across the organization.
• Develop architecture designs and patterns, partnering with engineering teams to ensure effective execution and delivery.
• Build and sustain close working relationships with engineering teams, ensuring the right skills are developed and aligned to support evolving technology direction.
• Champions modern architecture principles and technology modernization, bringing clear thought leadership and a forward-looking perspective to drive meaningful change.
• Proactively identifies opportunities to leverage emerging technology advances by analyzing key industry and technology trends and assessing their potential impact on the enterprise.
• Drive the design and execution of technology proof of concepts, ensuring modern architecture solutions are rigorously assessed for feasibility and fit before implementation.

Data Architect – Data and Analytics

• 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.

• Design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
• Develop and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
• Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
• Monitor work against production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
• Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations.
• Collaborate on the design and implementation of data security measures, including access controls, encryption, and data masking
• Mentor other associate and intermediate data engineers as needed
• Perform data analysis and provide insights to support decision-making across various departments
• Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed
• Other duties as assigned or requested.

• Lead, mentor, and develop a team of data engineers, fostering a culture of innovation, accountability, and continuous improvement.
• Execute the data engineering strategy in alignment with manufacturing and supply chain objectives.
• Design and implement scalable, secure, and resilient data architectures across cloud (Azure, AWS) and hybrid environments.
• Manage data acquisition and ingestion processes, including integration with PLCs, historians, SCADA, IoT, and manufacturing systems.
• Support and enforce data governance, standards, and best practices for data quality, lifecycle management, and compliance.
• Collaborate with IT, operations, quality, and supply chain teams to translate business requirements into actionable data solutions.
• Develop and maintain real-time and batch data pipelines using technologies such as Snowflake, PowerBI, Power Automate, MQTT, Inductive Automation, Tulip, and SAP.
• Provide insights into manufacturing KPIs and other critical metrics, ensuring data is contextualized and accessible for stakeholders.
• Drive continuous improvement in data engineering processes, tools, and team capabilities.
• Promote an action-oriented, inclusive, and collaborative team culture.
• Ensure solutions comply with organizational standards and strategic direction.
• Facilitate the sharing of ideas and best practices across the team using existing and emerging technologies.
• Provide regular updates and ongoing support to management and key stakeholders.
• Uphold high standards of work quality and contribute to the success of the team.

Senior Manager, Data Engineering

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• Collaborate with Product Management, Servicing, Sales, Fraud, Marketing, and UX teams to understand use cases and segmentation needs.
• Work closely with internal stakeholders to understand their data needs and translate those needs into user stories for Data & Analytics, technical, and engineering teams.
• Help develop a comprehensive CDP implementation strategy aligned with overall product and customer success goals.
• Identify new data sources and onboard existing data sources to the CDP.
• Evolve our identity resolution models (deterministic and probabilistic).
• Build and run queries to answer questions and build segment and build solutions for users of CDP to be able to self-serve.
• Be the CDP subject matter expert, gathering information to create accurate, informative, and resonant user stories for both the business and the data team.
• Deeply understand customer needs to define priorities, inspire the broader team, and deliver best-in-class experiences and enabling capabilities.
• Help build the product roadmap, define user stories, and drive trade-off decisions.
• Identify new products and features on behalf of the customer, leading 1-2 scrum teams to align on mission and deliver great results iteratively.
• Leverage qualitative and quantitative data to measure results, inform the roadmap, and achieve benefit, adoption, and financial results.
• Collaborate with Product, Analytics, Engineering, and Business Leaders to drive scalable data solutions.
• Present effectively to high-level stakeholders, set clear priorities and direction, and build bridges across groups.
• Own the product end-to-end by developing business models, defining and analyzing success metrics, and managing strategic projects.
• Champion and educate product managers and lines of businesses on what the GEICO CDP can do for their business.
• Lead the CDP Squad in achieving strategic outcomes with solutions that are Valuable, Viable, Usable and Feasible.

Principal Product Manager – Customer Data Platform

• Drive Databricks platform strategy and support AI‑driven initiatives across GDIT and federal health agencies
• Provide hands‑on technical and managerial leadership for a mission‑critical modernization and migration effort
• Lead the daily execution of a large‑scale, complex data‑migration initiative supporting federal health systems modernization
• Oversee extraction, transformation, mapping, profiling, validation, reconciliation, and synchronization of data across legacy and modern systems
• Lead, mentor, and guide multidisciplinary teams of data engineers and analysts

Data Architect, SME

• Design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
• Develop and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
• Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
• Monitor work against production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
• Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations.
• Collaborate on the design and implementation of data security measures, including access controls, encryption, and data masking
• Mentor other associate and intermediate data engineers as needed
• Perform data analysis and provide insights to support decision-making across various departments
• Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed

Senior Data Engineer – Cloud Data Products, Analytic Enablement

• Design and maintain ingestion, ETL, and activation pipelines supporting real-time and batch processing of multi-billion event datasets
• Optimize data workflows for latency, reliability, and scalability in cloud-native environments
• Modernize legacy ad-tech systems into open, lakehouse-based architectures (Iceberg, Delta, Hudi) for unified analytics and monetization
• Partner with Data Science to operationalize ML models for forecasting, optimization, and personalization
• Ensure downstream data delivery into ad servers, identity systems, and activation platforms is low-latency and privacy-compliant
• Act as a domain expert in distributed systems, streaming, and big data engineering
• Mentor junior engineers, share best practices, and elevate engineering standards across global teams

Senior Staff Data Engineer – Ad Tech, AWS

• Build and operationalize complex data solutions, correct problems, apply transformations, and recommending data cleansing/quality solutions.
• Design complex data solutions
• Perform analysis of complex sources to determine value and use and recommend data to include in analytical processes.
• Incorporate core data management competencies including data governance, data security and data quality.
• Collaborate within and across teams to support delivery and educate end users on complex data products/analytic environment.
• Perform data and system analysis, assessment and resolution for complex defects and incidents and correct as appropriate.
• Test data movement, transformation code, and data components.
• Perform other duties as assigned.

Data Engineer II – AWS, Databricks, SQL, GenAI

• Develop and execute a comprehensive data strategy, aligned with the organization’s goals, and focuses on digital engineering, digital thread, and advanced analytics across the EMS Service Line.
• Oversee the data models, aligning data models across engineering and manufacturing to support user needs and align with other service Lines and CIDO.
• Collaborate with engineering and business leaders to align data initiatives with program goals as well as enterprise data governance team.
• Develop and enable program data risks management process in concurrence with program risk management.
• Maintain knowledge of current and emerging Industry and government trends and technologies in data frameworks, management and analytics and ensure proper utilization where applicable.
• Display strong stakeholder management skills, including presenting solutions to senior leadership.
• Drive company-wide data strategy and innovation, aligning technical goals with business objectives.
• Influence and gain buy-in from key stakeholders, ensuring alignment and successful project delivery.
• Manage data governance, data security, data modeling, and enterprise architecture frameworks.

Senior Staff Systems Data Architect

• Design, build, and maintain scalable ETL pipelines using Python and PySpark within Palantir Foundry.
• Develop and refine "Objects" (e.g., Soldiers, Equipment, Units) and their relationships to ensure a clean, accurate digital representation of Army operations.
• Assist with the cleanup of legacy data models and implement automated data quality checks.
• Build low-code/no-code applications and dashboards that allow senior leaders to access high-quality data in real-time.
• Implement Medallion Lakehouse concepts to organize data into Bronze, Silver, and Gold layers for downstream analytics.

Data Engineer – Clearance Required

• Responsible for developing, expanding, and optimizing our data and data pipeline architecture
• Support software developers, database architects, data analysts, and data scientists on data initiatives and ensure optimal data delivery architecture is consistent throughout ongoing projects
• Creates new pipeline and maintains existing pipeline, updates Extract, Transform, Load (ETL) process
• Implements large dataset engineering: data augmentation, data quality analysis, data analytics, data profiling, and develop data strategy recommendations
• Operate large-scale data processing pipelines and resolve business and technical issues pertaining to processing and data quality
• Assemble large, complex sets of data that meet non-functional and functional business requirements
• Identify, design, and implement internal process improvements including re-designing data infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics
• Work with stakeholders including data, design, product, and government stakeholders and assist them with data-related technical issues
• Write unit and integration tests for all data processing code
• Work with DevOps engineers on CI, CD, and IaC
• Read specs and translate them into code and design documents
• Perform code reviews and develop processes for improving code quality.

Data Engineer III

• Collaborate with the Data and AI team to design and build data infrastructure, including ETL/ELT pipelines using Azure Data Factory.
• Develop and maintain data models, cloud data warehouses, and data lakes using Azure SQL Server and Azure SQL Database.
• Write and optimize T-SQL scripts, stored procedures, and functions for data transformation and integration.
• Support data quality, governance, and security initiatives.
• Participate in the full lifecycle of data projects, from requirements gathering to deployment and monitoring.

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

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Senior Data Engineer – Databricks, AWS

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Data Engineer

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Data Engineer

Senior Data Engineer

Senior Manager, Data Engineering

Data Engineer

Principal Product Manager – Customer Data Platform

Data Architect, SME

Senior Data Engineer – Cloud Data Products, Analytic Enablement

Senior Staff Data Engineer – Ad Tech, AWS

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Senior Staff Systems Data Architect

Data Engineer – Clearance Required

Data Engineer III

Data Engineer

Data Engineer