Data Engineer

• Lead the Platform Pod.
• Own the Cara roadmap across all phases.
• Design the Assistants and write the specs.
• Drive the infrastructure conversation in partnership.
• Own the MCP and API contract standards.
• Build design patterns that scale.
• Partner across engineering and prioritize in the open.
• Support go-to-market efforts.

Senior Platform Product Manager, Data & AI

• 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.

• Design and implement enterprise semantic and ontology models that enable advanced analytics, AI, and LLM-driven solutions.
• Establish and maintain enterprise data quality and validation frameworks that identify, monitor, and communicate data integrity issues, ensuring trusted and reliable data across the organization.
• Partner with business and technology stakeholders to design data pipelines and solutions that align with evolving business needs and strategic objectives.
• Architect scalable data engineering patterns and optimize data solutions for performance, scalability, observability, and long-term maintainability across data warehouses and lake houses.
• Develop and maintain CI/CD processes, deployment standards, and engineering best practices that support reliable and efficient delivery of semantic models and data solutions.
• Integrate data lineage, metadata management, and observability capabilities into data solutions to improve transparency, traceability, and operational effectiveness.
• Contribute to enterprise data governance, security, and compliance initiatives by implementing data access controls, including Row Level Security (RLS), and promoting adherence to organizational standards.
• Provide technical leadership through code reviews, mentoring, and knowledge sharing to elevate engineering practices and support the development of data modeling and analytics engineering capabilities.

Senior Data Engineer

• Building state of the art pipelines and data models that are at the heart of the studio decision-making process.
• Working on large-scale lakehouse and warehouse analytics systems that process data feeds in real-time and batch processes.

Lead Data Engineer

• Full-Cycle Recruiting: Own the end-to-end recruiting process for technical and product roles — intake, sourcing, screening, interview coordination, offer negotiation, and close.
• Strategic Sourcing: Build and execute creative sourcing strategies (LinkedIn Recruiter, Boolean/X-ray search, GitHub, niche communities, referrals) to identify passive candidates for hard-to-fill roles like AI Engineers and Data Scientists.
• Stakeholder Partnership: Partner closely with hiring managers across Engineering, Data, AI, and Product to define role requirements, calibrate candidate profiles, and build hiring plans that scale with the business.
• Candidate Experience: Act as a brand ambassador for Stord, delivering a thoughtful, transparent, and fast-moving experience for every candidate, regardless of outcome.
• Technical & Product Assessment: Develop enough fluency in engineering, data, and product domains to accurately screen for technical depth, product sense, and role fit — asking sharp, informed questions in early-stage conversations.
• Pipeline & Market Insight: Track pipeline health, conversion metrics, and time-to-fill; bring market intelligence (compensation trends, competitor movement, talent availability) back to hiring teams.
• Process Improvement: Continuously refine interview processes, scorecards, and workflows to reduce time-to-hire while maintaining a high talent bar.
• Cross-Team Collaboration: Work with People Ops, Compensation, and other TA Partners to ensure consistent, equitable, and compliant hiring practices across the org.

Talent Acquisition Partner – Product, Data, Engineering

• Serve as AG's primary technical counterpart during vendor-led delivery engagements: review architecture decisions, validate implementation against AG requirements, and own knowledge transfer to ensure internal capability is built
• Build and optimize cloud-based data pipelines, data lakes, data warehouses, and ETL frameworks using cloud-native tooling across major platforms (Azure, AWS, or equivalent)
• Ensure system integrations are scalable, secure, and aligned with compliance standards
• Partner with Team Innovation and functional department leaders to establish data standards, governance, and documentation
• Implement version control, CI/CD, and data workflow automation practices
• Design, build, and automate data models, ingestion pipelines, and transformation workflows across source systems and Medallion layers
• Implement monitoring processes for data quality, validation, and integrity across all platforms
• Design and build external client portal data layer: implement Row-Level Security (RLS) in Power BI, manage client-scoped semantic models, and support embedded analytics delivery to external stakeholders
• Monitor and manage Azure cloud resource costs: right-size compute clusters, implement resource tagging, configure budget alerts, and report on consumption trends
• Troubleshoot and optimize performance across APIs, integrations, and data warehouse environments

Senior Data Architect, Team Innovation

• Own the architecture of our data platform, spanning ingestion (Hevo, Estuary), our Google BigQuery warehouse, and the dbt transformation layer — designing systems that are scalable, reliable, secure, and built to last.
• Set the technical bar as a senior IC, leading through design reviews, code review, and mentorship — raising the quality, rigor, and engineering discipline of the team without a formal management role.
• Build and harden data ingestion at scale, designing batch and CDC pipelines across a diverse set of sources — operational databases, SaaS applications, and ERP systems — and driving vendor and tooling decisions with a clear point of view.
• Develop end-to-end with AI coding agents. Our teams build with Claude Code and similar tools, and you'll help define the practices, guardrails, and standards that make AI-assisted engineering fast, safe, and high-quality.
• Invest in reliability and observability, establishing testing, monitoring, alerting, and incident-response practices so data lands on time, is trusted, and failures are caught before stakeholders notice.
• Strengthen governance and controls, implementing the access models, auditability, and operational rigor expected of a company operating at scale and under growing scrutiny.
• Lay the foundations for agentic analytics, building the well-modeled, well-governed platform that agents and MCP connections rely on to deliver trustworthy answers from data.
• Partner with analytics engineers, analysts, and stakeholders to understand data needs, unblock high-value use cases, and translate business requirements into durable platform capabilities.
• Engineer tooling and services beyond the warehouse - building the APIs, automated jobs, and internal tools in Python that make the platform run smoothly and put data in more people's hands than SQL alone can reach.
• Drive the evolution of our warehouse architecture - leading hands-on POCs on lakehouse patterns, open table formats, and alternative query engines, and deciding what's worth adopting next.

Staff Data Engineer

• 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.

• Build and maintain the cloud infrastructure, data pipelines, and deployment workflows
• Calculate measure ratings for the med adherence and avoidance measures
• Collaborate with data scientists, product owners, and business stakeholders
• Deliver scalable, well-architected solutions that move insights from development to production

Senior GCP Data Engineer – Patient Safety Measure Engine

• 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

• Create and improve workflows for simulation data generation, storage, traceability, analysis, and reuse
• Build automation pipelines for simulation execution, post-processing, reporting, and data quality checks
• Use GitHub, GitHub Actions, Databricks, and SQL-based tools to manage datasets and automate workflows
• Partner with simulation engineers and cross-functional teams to connect models, data, and analysis into efficient end-to-end workflows
• Standardize data structures, metadata, documentation, and best practices to improve speed, consistency, and reliability
• Drive cultural change toward data-driven, automated simulation workflows through example implementations, training, and ongoing guidance

Vehicle Dynamics Simulation Engineer

• 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

• Own and deliver projects end to end, from scoping and system design through implementation, rollout, and production validation, driving measurable progress on the team's highest-priority initiatives.
• Design and build high-reliability, low-latency systems serving millions of users, tackling challenges like caching, upstream service optimization, and efficient connection management at scale.
• Build and improve the API framework and tooling that hundreds of engineers depend on, making it fast and easy for teams across the company to build, test, and ship independently.
• Drive operational excellence across T0 services: own SLOs, improve observability, lead incident response, and reduce operational toil so the team can invest in high-leverage work.
• Partner cross-functionally with product engineering teams to design API schemas, support service launches, and ensure the platform evolves to meet their needs.
• Set the technical bar through code and design reviews, mentor engineers across the team, and contribute to architectural direction.

Senior Software Engineer, Data Layer

• Own the product strategy and roadmap for Tanium’s cloud/SaaS data platform
• Define and drive the short- and long-term roadmap for Tanium’s end-to-end data architecture
• Partner with Product Managers, Engineering, Design, and leadership across the organization
• Lead product direction for how data is surfaced and experienced in Atlas and the UI
• Drive the strategy for data movement and integration capabilities
• Establish clear product requirements, technical specifications, use cases, business logic, and success metrics
• Partner with engineering leadership to make durable architectural decisions
• Champion data quality as a core product priority
• Collaborate with internal teams on enablement, training, and launch materials
• Work closely with business, technical, customer-facing, and go-to-market teams
• Leverage innovation and trends in cloud, SaaS, data architecture, analytics, and enterprise platform technologies to develop strategic solutions
• Build, lead, and develop a high-performing product team

Director, Product Management – Data Platform

• This is a remote position.
• We are seeking a Data Engineer with 5+ years of experience to build and scale data platforms powering AI-driven analytics on high-volume sensor, satellite, and third-party data.
• The ideal candidate will have expertise in designing and managing scalable batch and real-time data pipelines using Apache Airflow, Apache Spark, Kafka, Python, and SQL.
• Strong experience with PostgreSQL, TimescaleDB, PostGIS, cloud-based data lake architectures, and geospatial/time-series data is essential.
• The role involves owning data pipelines end-to-end—from real-time ingestion to analytics—while ensuring data quality, performance, observability, and reliability in a cloud-native environment.
• Strong stakeholder management, problem-solving, and collaboration skills are key to delivering impactful data products and analytical solutions.

Data Engineer – AI-Driven Data Platforms

• Define and drive the data science and AI roadmap, aligning model development priorities with business objectives and product strategy.
• Lead end-to-end delivery of ML and AI solutions — from problem framing, data discovery, and model design through validation, deployment, and performance monitoring.
• Translate ambiguous business problems into well-scoped data science workstreams, identifying quick wins alongside longer-term strategic initiatives.
• Champion best practices in model development, including versioning, documentation, validation, and observability.
• Design and implement NLP pipelines for use cases such as entity extraction, semantic mapping, classification, and retrieval-augmented generation (RAG).
• Build and maintain forecasting and predictive models to support operational and strategic decision-making.
• Apply statistical and machine learning methods to identify root causes of process inefficiencies and data quality issues.
• Develop reusable data pipelines, crosswalk tables, and transformation workflows that support scalable, cross-functional data products.
• Conduct current-state assessments of data architecture, sources, and quality; define future-state data models and governance standards.
• Develop and maintain KPI reporting frameworks and dashboards that enable performance monitoring and data-driven decision-making.
• Apply process optimization methodologies (e.g., Lean Six Sigma) to identify bottlenecks, reduce cycle time, and improve data accuracy.
• Ensure analytical outputs are accurate, auditable, and aligned with regulatory and compliance requirements (e.g., HIPAA, GDPR).
• Partner closely with Product, Engineering, and business stakeholders to clarify requirements, validate feasibility, and define measurable success criteria.
• Communicate complex analytical findings and model outputs clearly to both technical and non-technical audiences, including executive stakeholders.
• Define value-realization strategies for data and AI investments, ensuring ROI is tracked through improved search, reporting, and operational insight.
• Mentor data analysts and junior data scientists through pairing, design reviews, and structured technical guidance.
• Lead knowledge transfer of owned models, pipelines, and analytical frameworks to ensure team resilience and continuity.
• Drive a culture of continuous learning, analytical rigor, and responsible AI within the data science function.

Principal Data Engineer

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Build and optimize data architectures and warehouses (e.g., Snowflake, BigQuery Etc).
• Integrate data from a wide variety of sources using APIs and web browser automations.
• Collaborate with company leadership to understand data needs and implement ongoing improvements in system processes.
• Monitor and ensure data quality, reliability, and performance.
• Automate data workflows and implement data validation and testing processes.
• Maintain up-to-date documentation for data pipelines, systems, and processes.

• Build our data and AI foundation: Design how operational and financial data is captured, structured, processed, stored, and queried across Renterra. This includes event architecture, data pipelines, and analytical systems such as ClickHouse.
• Create AI-powered products: Build agents and intelligent workflows that use Renterra’s data to identify problems, answer questions, support decisions, and automate work for our customers.
• Own production quality: Make data and AI systems reliable, observable, secure, and cost-effective. Design for failure recovery, data quality, permissions, model uncertainty, and appropriate human review.
• Measure what works: Develop evaluations and feedback loops for accuracy, reliability, latency, cost, and customer impact. Use what you learn to improve both the underlying data systems and the product experience.
• Work across the company: Partner with leadership, product, engineering, support, and customers to find valuable problems and turn them into shipped solutions.
• Make pragmatic technical decisions: Choose the right approach for each problem, whether that is a deterministic workflow, analytical query, machine-learning model, LLM, vendor, or a combination of them.
• Report directly to the CTO.

Senior Software Engineer, AI & Data

• Design, build, and debug the highest-impact pipelines, connecting our business systems, including CRM, billing, and internal platforms to our Snowflake warehouse, in both directions
• Own the data strategy and architecture across a multi-year horizon, while maintaining deep, hands-on familiarity with the systems and their edge cases
• Establish technical direction and standards for pipeline design, data modeling, and data quality across the org; personally build and ship the highest-risk or most ambiguous work
• Build, structure, and lead a growing Data organization of 5+ across Data Engineering, ML Engineering, and Analytics
• Own headcount planning, hiring strategy, and budget for the Data organization
• Partner with the Chief Product and Technology Officer and cross-functional leaders to translate company strategy into data contracts and platform investments
• Lead audits of existing integrations and automations, and determine what should be replaced and how that work should be prioritized
• Champion AI and automation enablement company-wide, ensuring agents and LLM use cases are built on a trusted data foundation, while remaining hands-on in building select enablement capabilities

Director, Data Engineering

• Define current-state and future-state data architecture across enterprise platforms and applications.
• Design scalable data foundations across cloud and hybrid environments.
• Establish standards across data ingestion, storage, transformation, modeling, metadata, lineage, quality, and governance.
• Create architecture patterns for batch, streaming, event-driven, and API-based data integration.
• Develop conceptual, logical, and physical data models aligned with business and technical requirements.
• Assess existing data platforms and identify gaps across scalability, quality, governance, integration, security, and accessibility.
• Partner with engineering, product, analytics, security, and business teams to ensure enterprise data is trusted, usable, and properly governed.
• Support data architectures that enable analytics, generative AI, retrieval-augmented generation, and other emerging technology use cases.
• Evaluate data platforms, tools, and technologies based on scalability, security, maintainability, and business needs.
• Translate complex technical concepts into clear recommendations, roadmaps, and architecture documentation for technical and executive stakeholders.

Data Engineer jobs in United States

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Senior Platform Product Manager, Data & AI

Data Engineer

Senior Data Engineer

Lead Data Engineer

Talent Acquisition Partner – Product, Data, Engineering

Senior Data Architect, Team Innovation

Staff Data Engineer

Senior Data Engineer

Senior GCP Data Engineer – Patient Safety Measure Engine

Director, Data Engineering – Business Data

Vehicle Dynamics Simulation Engineer

Senior Data Loss Prevention (DLP) Architect, Engineer

Senior Software Engineer, Data Layer

Director, Product Management – Data Platform

Data Engineer – AI-Driven Data Platforms

Principal Data Engineer

Data Engineer

Senior Software Engineer, AI & Data

Director, Data Engineering

Data Architect