Data Engineer

• Develop and manage data engineering solutions involving Databricks Lakehouse Platform
• Collaborate with clients and stakeholders to gather requirements and feedback
• Ensure data integrity and implement data governance practices
• Design architecture for data processing and storage solutions
• Work in consulting environments to deliver services and solutions to clients

• 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

Senior Data Engineer

• Design and implement system architectures spanning cloud infrastructure, platforms, and applications.
• Translate mission, security, and operational requirements into practical technical designs.
• Develop and maintain reference architectures, design patterns, and system blueprints.
• Evaluate architectural tradeoffs involving scalability, reliability, security, and cost.
• Support architecture reviews and guide teams toward sustainable system designs.
• Build and operate cloud-native systems using infrastructure-as-code and automation.
• Develop prototypes and proofs of concept to validate architectural approaches.
• Contribute directly to complex implementations when systems cross team or domain boundaries.
• Troubleshoot and resolve high-impact production and integration issues.
• Support database and platform-level concerns in partnership with cloud and data engineers.
• Collaborate with Cloud Engineering and Build Platform teams to define secure, scalable platforms.
• Help establish guardrails, golden paths, and secure-by-default patterns.
• Integrate security, compliance, and observability requirements into system designs.
• Partner with ISSOs to translate policy and RMF controls into implementable solutions.
• Serve as a technical mentor and force multiplier for engineers across teams.
• Influence technical roadmaps and platform evolution.
• Participate in building the long-term architecture and platform function, with growth toward Principal-level scope as the team expands.

Staff Software Engineer – Data & Architecture

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

Senior Software Engineer, Data

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

Senior Software Engineer, ERP, Data & Domain

• Own the 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

• Develop, implement, and maintain enterprise data management solutions
• Assist with the development, implementation, and maintenance of an overall organizational data strategy
• Design and build data processing flows to extract data from various sources
• Load data into data storage systems
• Transform data using industry-standard techniques

• Work collaboratively with the Managing Consultant, customer data owners, and other Encompass project stakeholders to define data migration activities and schedule for new customer onboarding.
• Owns the migration of key customer data to drive the ECP system configuration and populate historical transactional data required for customers to go-live. This includes directly extracting data from legacy systems, working with customer stakeholders to populate import templates, importing the data into ECP, validating the data for accuracy and completeness, and working with the customer and project team to resolve issues.
• Complete planned activities on time and within budget, consistent with the onboarding project plan, and to our standard of quality.

Data Migration Consultant

• Architect and build scalable, secure, and observable data infrastructure to power LLM-based agents, multi-agent systems, and tool-using AI workflows.
• Design and operate robust batch and real-time data pipelines supporting embeddings, RAG systems, and agent memory frameworks.
• Develop and manage vector database solutions to enable low-latency retrieval and contextual intelligence for AI applications.
• Build data frameworks for training, evaluation, benchmarking, and continuous improvement of agentic AI systems.
• Implement strong data governance, quality controls, lineage tracking, and PII/security compliance across AI data platforms.
• Collaborate with AI/ML, platform, and DevOps teams to productionize experimental AI prototypes into enterprise-grade solutions.
• Optimize data systems for performance, scalability, reliability, and cost efficiency across cloud environments (AWS, Azure, or GCP).

Senior Data Engineer – Agentic AI Engineering

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

Staff Software Engineer – DC Tech Lead

• Own the end-to-end roadmap for Invoca's data platform and intelligence products
• Define and launch intelligence products – signals, scores, and insights – that create measurable value for both internal teams and paying customers
• Define how context is structured, staged, and delivered to AI agents across voice, SMS, and web – ensuring every interaction is grounded in the right data at the right time
• Own the intelligence and data products that other teams and customers use to build unique experiences on top of Invoca data
• Drive cross-team execution across teams working on the agentic platform, conversational AI products on voice and SMS, signals, and insights – managing dependencies, driving prioritization, and keeping work unblocked
• Build data applications and agents on our data lake that power product use cases across the business – from agent performance measurement to customer outcome analytics
• Partner with data engineers and ML teams to make principled tradeoffs across data freshness, coverage, latency, and cost
• Define success metrics for data products and continuously measure impact through adoption, coverage, agent performance, and quality signals
• Evaluate build vs. buy decisions for data infrastructure and guide vendor integrations behind stable, well-defined contracts
• Stay current on advancements in retrieval, grounding, and context injection patterns – translating signal into pragmatic product decisions

Senior Product Manager, Data Platform – Intelligence

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

• Solving our unique data-lake challenges: transforming and seamlessly normalizing highly varied partner datasets (such as donation data);
• Design robust batch-processing pipelines capable of extracting and loading massive datasets from a variety of internal, external, and public sources;
• Developing processes for data mining, data modeling, and data production;
• Leveraging modern technology knowledge to champion evolving industry trends, updated design patterns, and engineering excellence across the team;
• Autonomously scope, design, and execute complex data projects from day one, turning ambiguous requirements into clear, decisive technical plans;
• Developing robust testing and monitoring systems for scheduled processes;
• Collaborating with cross-functional teams to support their data infrastructure needs;
• Joining each and every one of your colleagues in creating an inclusive workspace.

• Serve as the functional DBA for Oracle and MongoDB across production and non‑production environments
• Partner with application teams on schema design, data modeling, and query optimization
• Analyze and resolve database-related functional issues, performance bottlenecks, and data anomalies
• Support release activities by validating database changes, scripts, and backward compatibility
• Provide guidance on transaction management, indexing strategy, and data access patterns
• Ensure data consistency, integrity, and correctness across applications and integrations
• Review and approve database changes for functional risk and impact
• Assist incident response by diagnosing database behavior and data-related failures
• Maintain functional documentation, standards, and runbooks for database usage

Data Engineer – Oracle, MongoDB DBA Specialist

• Collaborate with key data and technology stakeholders to build out the enterprise data architecture practice
• Establish data architecture models and standards
• Guide teams to build out semantic and object standards for data taxonomies, models, flows, and diagrams
• Produce and maintain enterprise data architecture artifacts and support governance
• Create data reference architectures and document data flows
• Lead development of data modeling standards, best practices, and guidelines
• Oversee metadata, master data, and reference data management
• Measure progress against objectives with follow-up

Senior Enterprise Architect, Enterprise Data Architect

• Collaborate with other Engineers and Architects to define the enterprise data architecture roadmap.
• Drive the design and optimization of OLTP and OLAP models for scalability, performance, and maintainability.
• Establish standards for data modeling, storage, and integration across systems.
• Partner with other key stakeholders within engineering in developing a comprehensive data and BI strategy aligned with business objectives.
• Define data governance practices, data access control, compliance, and quality standards.
• Lead proof-of-concepts (POCs) for new data technologies and frameworks.
• Own the architectural review process and guidance to development teams on data-related projects.
• Establish and ensure best patterns and practices for ORM usage (Entity Framework and others) and SQL optimization, according to business needs.
• Evaluate and recommend where AI/ML can enhance data-driven decision-making.
• Stay current with modern data engineering trends, cloud-native architectures, and big data platforms.
• Actively participate and take ownership of Data Architecture artifacts via ADR process.
• Drive AI adoption by introducing and guiding teams in leveraging AI capabilities within the data engineering space to enhance internal efficiency, innovation, and decision-making in everyday workflows.
• Liaison between technology and product teams assisting teams in translating business requirements into tangible architectures that meet business needs now in and in the future.
• Design data architectures that accurately reflect and support evolving complex product and business processes and are required by our customer base.
• Lead data architecture initiatives, collaborating with cross functional teams, from concept through implementation, including governance and quality frameworks.
• Identify opportunities for predictive analytics and advanced modeling.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

Data Architect

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support.
• Own delivery, quality, release discipline, and execution of the technical roadmap.
• Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning.
• Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness.
• Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP.
• Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap.
• Push practical AI use across coding, review, testing, ops, and engineering productivity.

Director of Engineering – Data Platform

• Lead the design and evolution of Foley’s enterprise data architecture
• Contribute to the ongoing development of our Enterprise Data Model
• Define standards and best practices for data modeling, storage, access, and reliability
• Drive architectural decisions that balance scalability, maintainability, and performance
• Design, build, and maintain scalable data pipelines across internal systems, vendor platforms, and the data warehouse
• Improve data quality, observability, and reliability across core workflows
• Optimize data workflows for efficiency and long-term operational health
• Support both batch and event-driven or CDC-based pipeline patterns
• Own schema design and warehouse modeling strategy, including dimensional and normalized approaches where appropriate
• Help define and implement data governance best practices, including RBAC
• Ensure shared definitions, documentation, and trust in core business data
• Serve as a thought partner to Engineering, Product, Analytics, and business teams
• Translate technical complexity into clear recommendations and tradeoffs
• Raise the bar for data engineering practices across the organization
• Help the company make better decisions through strong, accessible data foundations
• Identify opportunities to improve workflows through automation, tooling, and thoughtful use of AI
• Experiment pragmatically with new technologies and scale what works
• Build systems and guardrails that enable teams to move faster without sacrificing quality

• Define and drive enterprise data architecture strategy
• Establish target-state data platform architectures and standards aligned to business priorities.
• Translate business requirements into scalable data architecture solutions.
• Design and govern enterprise data ecosystems.
• Architect data solutions and integration strategies across systems.
• Ensure consistent access, integration, and consumption of data across platforms.
• Lead data architecture governance and quality initiatives.
• Define standards for data quality, metadata, master data, and lifecycle management.
• Establish data governance policies, compliance controls, and security practices.
• Drive platform and technology decisions.
• Evaluate and recommend data platforms, tools, and emerging technologies.
• Define standards for data integration, pipelines, and data services.
• Collaborate across business, data, and technology teams.
• Partner with stakeholders to align data architecture with enterprise objectives.
• Act as a bridge between data strategy and technical execution.
• Provide leadership and oversight.
• Lead architecture reviews and ensure adherence to standards.
• Support delivery teams through the product lifecycle.

Enterprise Data Architect

• Write efficient, scalable, and high-performance code to meet the demands of our software applications, optimizing algorithms and data structures for speed and reliability.
• Design and implement elastic code architectures that enable our software systems to scale horizontally and vertically in response to changing demands.
• Architect and implement scalable and reliable data storage solutions to meet the organization's needs.
• Work closely with the AI/ML team providing format appropriate data delivery in support of business intelligence initiatives.
• Evaluate and select appropriate storage technologies, databases, and data warehouses to meet the organization's needs for data retention, archival, and analysis.
• Design and implement data ingestion pipelines to pull data from various sources into ElasticSearch for real-time analytics, search, and visualization.
• Optimize data ingestion workflows for scalability, reliability, and fault tolerance, considering high volume and velocity of incoming data streams.
• Collaborate with cross-functional teams to integrate data pipelines with the organization's portal and user interfaces, enabling seamless data access and visualization for end users.

Data Engineer jobs in Colorado

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Data Engineer

Senior Data Engineer

Staff Software Engineer – Data & Architecture

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Staff Data Engineer

Senior Data Engineer

Data Migration Consultant

Senior Data Engineer – Agentic AI Engineering

Staff Software Engineer – DC Tech Lead

Senior Product Manager, Data Platform – Intelligence

Senior Data Engineer

Staff Data Engineer

Data Engineer – Oracle, MongoDB DBA Specialist

Senior Enterprise Architect, Enterprise Data Architect

Data Architect

Director of Engineering – Data Platform

Staff Data Engineer

Enterprise Data Architect

Senior Software Engineer – Data Engineering