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.

Senior Data Engineer

• Build advanced data pipelines utilizing the Medallion Architecture to create high-quality data sources in Snowflake.
• Leverage AI-assisted development tools to reduce manual pipeline work and explore emerging Snowflake capabilities like Cortex to support AI/ML-enabled workflows.
• Build Data Applications that contribute to interactive experiences, including Streamlit-based apps, transforming Snowflake from a warehouse into a full-service data platform.
• Create robust data pipelines using AWS technologies to serve specific business needs.
• Design and evolve analytical data models that support BI, advanced analytics, and AI workloads.
• Partner directly with Analytics and Data Science teams to translate complex business questions into well-structured, modular data assets.
• Write complex Python/SQL, scripts, and stored procedures to ensure 99.95% uptime and optimize query performance for downstream consumption.
• Serve as a primary advisor to the Data Management team to identify opportunities for technical improvements and automation.
• Act as a leader for junior and mid-level engineers, providing direction on programming best practices, query optimization, and business tact.

Senior Data Engineer – Modern Data & AI Platforms

• 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, build, and maintain robust ETL/ELT pipelines that move data from diverse source systems into analytics, AI, and machine learning platforms.
• Develop and optimize data architectures that support real-time and batch analytics, ML workflows, and personalization.
• Partner with data architects, analysts, and client stakeholders to translate business requirements into technical designs.
• Ensure data quality, lineage, observability, and security through validation, monitoring, and governance best practices.
• Optimize performance and cost-efficiency of pipelines and storage.
• Support cloud-first architectures across AWS, Azure, and GCP.

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

Senior Software Engineer, ERP, Data & Domain

• Design, develop and test enterprise MarTech platforms for data engineering using SQL, Azure Data engineering skills including Azure Data Factory, Databricks/Fabric technologies
• Proficiency in Azure-based cloud technologies to support data needs, along with working in marketing projects (Adobe Experience Platform and/or Salesforce Marketing Cloud platform)
• Leverage cutting-edge data technologies, programming languages, and industry-standard coding practices to innovate new features and optimize existing product/marketing functionalities
• Create and execute unit tests, troubleshoot issues, and resolve defects efficiently
• Collaborate with Product, architects and cross-functional teams to align on requirements and implementation strategies
• Translate business and functional requirements into clear technical specifications and product deliverables
• Participate in technical design discussions and conduct code reviews to ensure quality and consistency
• Document system architecture, design approaches, and development processes for future reference
• Develop and maintain unit test plans and alpha test plans to support product validation
• Stay current with emerging technologies, tools, and methodologies to continuously improve design, development, and deployment practices.

Senior Data Engineer – Marketing Technologies

• Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
• Design, build, and operate batch, micro-batch, and streaming pipelines feeding Snowflake — Prefect-orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
• Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version-controlled documentation.
• Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no ClickOps in production.
• Build AWS-native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
• Maintain CI/CD pipelines (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review.
• Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; right-size before scaling up.
• Design RBAC, masking policies, and row-access policies that satisfy a regulated operator without becoming an access bottleneck.
• Own freshness SLAs and data contracts for the gold layer; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end-to-end.
• Collaborate with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

• 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

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

Data Engineer II

• 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

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Senior Principal, Data Engineering

• 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.

• 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 and support the organizational data integration efforts by effectively developing and leading a team of data integration developers, engineers, architects, and managers.
• Establish enterprise data architecture standards, canonical data models, data domains, and data product strategies.
• Lead the modernization of legacy SQL Server ETL workloads into Azure Databricks and Lakehouse architectures.
• Define and govern Bronze, Silver, and Gold data layer standards.
• Establish enterprise data dictionaries, business glossaries, metadata management, and lineage standards.
• Lead development of AI-ready data products supporting machine learning, predictive analytics, intelligent automation, RAG, and agentic AI solutions.
• Define enterprise DataOps practices including CI/CD, automated testing, observability, data quality, and deployment automation.
• Lead the design and implementation of data integration and data lake house solutions.
• Lead collaboration efforts with IT teams to ensure robust and scalable data architecture is established and meeting company objectives.
• Lead the establishment of data validation and reconciliation processes to maintain data accuracy.
• Partner with business stakeholders to understand data requirements and deliver solutions that meet their needs.
• Assess the current data services processes, identify challenges, quantify the business value, establish procedures to address challenges, and support the future state model and vision definition.
• Stay current with industry trends and advancements in data integration and management technologies.
• Lead healthcare data integration initiatives involving claims, eligibility, pharmacy, clinical, financial, operational, and partner data sources.
• Serve as technical authority for data modeling, canonicalization, master data management, and enterprise data governance practices.
• Provide direct leadership, coaching, hiring, and performance management for Data Engineers and Data Integration Engineers.
• Participate in architecture reviews and remain actively engaged in solution design, platform modernization, and technical delivery.
• Develop training plans to foster growth and development across functional areas to meet expanding technology needs.
• Research and develop learning needs for ongoing system developments with contractors.
• Continuously review and monitor technology resources and gap analysis, establish criteria and make recommendations for advancement.
• Develop and implement a comprehensive data management strategy aligned with strategic objectives.
• Establish resources, tools and direction for each functional leader.
• Establish data integration policies and procedures to ensure data accuracy, security, and compliance with regulatory requirements.
• Participate in, adhere to, and support compliance, people and culture, and learning programs.
• Perform other duties as assigned.

Manager, Data Engineering

• Design and own canonical data models and mapping governance across ERP, PDM and MOM domains
• Design migration architecture: landing/staging zones, bulk vs CDC patterns, APIs, message flows, and storage strategy for large binaries
• Create and maintain the mapping registry, reusable transformation modules, naming/versioning conventions and survivorship rules
• Specify reconciliation, lineage and audit frameworks and embed them into migration pipelines
• Lead CI/CD and automation strategy for migration artifacts
• Collaborate with domain leads and technical leaders to validate connector behavior
• Implement data security controls in migration pipelines
• Performance migration flows: sizing, throughput tuning, batching strategies, and large-file transfer optimization
• Produce architecture diagrams, deployment artifacts and documentation

Data Migration Architect – Senior/Lead

• Lead the strategy, mapping, reconciliation, and long-term stewardship of ERP data across key enterprise systems
• Partner closely with Finance, Procurement, Manufacturing, Master Data, Supply Chain, Product Data Management (PDM), and Manufacturing Operations Management (MOM) teams
• Develop and own master data mapping registry, survivorship rules and harmonization strategy for materials, vendors, customers, plants, units of measure and classification
• Translate legacy ERP customization into S/4 data model mappings and recommend target design adjustments where necessary
• Define and enforce data quality rules; approve remediation approaches for ERP data
• Ensure migration artifacts support audit and compliance requirements (mappings traceability, change logs, lineage evidence)
• Document mappings, playbooks and handover artifacts

ERP Data Migration Lead

• Build and maintain detailed project plans for data migration pilots and data transformation
• Coordinate day-to-day activities of contractors, vendor purchase services and internal Subject Matter Experts (SMEs); validate deliverables against acceptance criteria and Statements of Work (SOWs)
• Track data migration Key Performance Indicators (KPIs) and produce executive ready reporting (reconciliation rates, throughput, defect trends, cutover adherence)
• Drive issue resolution: facilitate remediation actions, prioritize work with data leads, and escalate unresolved critical items to Senior Manager
• Enforce schedule and decision Service Level Agreements (SLAs) to keep data migration cycles on track; coordinate resource leveling and contractor allocation
• Capture lessons learned, update runbooks/templates and drive continuous improvement across data transformation cycles

Data Migration Project Manager – Senior

• Develop and implement scalable, secure, and interoperable data systems (including data warehouses, data lakes, and real-time processing platforms) tailored for OU Health’s enterprise data analytics platform.
• Define and maintain conceptual, logical, and physical data models to standardize data across clinical, research, and operational systems.
• Ensure models support healthcare-specific requirements (clinical, claims, provider, member, etc.).
• Design and oversee data integration processes (ETL/ELT pipelines) to enable seamless data flow between OU Health and other entities as needed.
• Work closely with clinical, research, and administrative stakeholders to gather requirements and deliver data solutions that meet departmental needs.
• Facilitate effective communication and collaboration among all parties.
• Monitor and optimize database performance, troubleshoot issues, and integrate new technologies to support evolving healthcare analytics needs.
• Implement robust security and backup procedures to protect sensitive patient information and ensure compliance with privacy laws.
• Prepare and maintain system documentation, data dictionaries, and architecture blueprints.
• Provide training and guidance to team members on data management best practices.
• Identify opportunities to enhance data infrastructure, support innovation, and enable advanced analytics for improved patient outcomes and operational efficiency.
• Work closely with clinical staff, IT professionals, data analysts, and researchers to understand data requirements, workflows, and pain points.
• Facilitate regular meetings and workshops to gather input and align data solutions with clinical and operational needs.
• Act as a liaison between technical teams and non-technical stakeholders, translating complex data concepts into actionable insights.
• Ensure that data architecture decisions reflect the priorities of clinicians, administrators, and compliance officers.
• Promote a culture of knowledge sharing by documenting data models, standards, and best practices.
• Provide training and mentorship to team members on data management, security, and governance.
• Lead collaborative efforts to troubleshoot data issues, optimize workflows, and implement new technologies.
• Encourage open communication and feedback to continuously improve data systems and processes.
• Participate in project planning and execution, ensuring that data architecture aligns with project goals, timelines, and resource constraints.
• Coordinate with project managers and other architects to deliver integrated solutions.

Data Engineer jobs in Montana

See all jobs on JobTailor

Senior Data Engineer

Senior Data Engineer – Modern Data & AI Platforms

Senior Software Engineer, Data

Data Engineer

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer – Marketing Technologies

Senior Data Engineer

Staff Data Engineer

Data Engineer II

Staff Software Engineer – DC Tech Lead

Senior Principal, Data Engineering

Senior Data Engineer

Staff Data Engineer

Data Architect

Director of Engineering – Data Platform

Manager, Data Engineering

Data Migration Architect – Senior/Lead

ERP Data Migration Lead

Data Migration Project Manager – Senior

Enterprise Data Architect