Data Engineer

• 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

•The Data Architect will help design, build, and evolve our next‑generation enterprise data platform & data integration pipeline.
•This role is central to our transformation toward a modern, governed, cloud‑based data ecosystem that supports all functions at Coverys (notably underwriting, claims, actuarial, finance, and enterprise analytics etc.).
•The Data Architect will help define the architectural blueprint, establish data modeling standards, and guide the development of scalable, automated data pipelines in Snowflake.
•In addition to design of the architecture, this position will work closely with business and technology teams to ensure data is accurate, trusted, and available for advanced analytics, reporting, and operational decision‑making.
•Design and maintain enterprise data architecture aligned to business domains (Policy, Party, Claims, Billing, Underwriting, etc.).
•Ensure data architecture design aligns to enterprise architecture standards & partners closely with Enterprise Architecture.
•Develop canonical and semantic data models to support analytics, reporting, and operational use cases.
•Define standards for data modeling, data quality, naming conventions, metadata, lineage, and documentation.
•Partner with business stakeholders to translate requirements into scalable data structures.
•Build ELT/ETL pipelines using modern cloud-native tools and frameworks.
•Lead the migration from legacy batch processes to automated, event-driven, or CDC-based ingestion patterns.
•Implement data quality rules, validation frameworks, and reconciliation logic.
•Optimize Snowflake workloads for performance, cost, and reliability.
•Design and oversee medallion-style data layers (bronze/silver/gold) for ingestion, curation, and consumption.
•Work with Data Governance to establish data dictionaries, lineage, classification, data quality and stewardship models.
•Ensure consistent use of canonical identifiers across systems and domains.
•Promote data-as-a-product principles and guide teams toward reusable, scalable data assets.
•Partner with business analysts, data scientists, actuaries, and analytics teams to support data needs.
•Mentor SQL developers and analytics engineers transitioning into modern data engineering roles.
•Mentor and coach the data engineering team & lead the development of pipeline and architecture platforms.
•Provide architectural oversight for major data initiatives across the enterprise.
•Support evolving business needs, as applicable.

Data Architect

• Apply Machine Learning to Powertrain Data: Develop, train, and deploy machine learning models on curated powertrain data to detect anomalies, identify early-warning quality indicators, and predict component degradation.
• Quantify & Assess Risk: Use statistical modeling and ML inference to quantify, assess, and prioritize risks associated with powertrain field quality issues, enabling data-driven decision-making.
• Perform Inferential Analytics: Conduct inferential and diagnostic analytics to identify root causes of complex engineering and quality problems, translating CV big data into actionable insights.
• Establish Stakeholder Alignment: Build strong working relationships with key stakeholders in Product Development to ensure that plans and requirements are fully understood, and issues are resolved effectively and efficiently.
• Debug & Resolve Issues: Debug, root-cause, and resolve propulsion systems quality issues with cross-functional teams, leveraging connected vehicle data, ML models, and enterprise toolsets.
• Foster Data Collection: Drive and optimize connected vehicle data collection strategies for solving engineering problems and characterizing customer usage patterns.
• Query & Manipulate Big Data: Write highly proficient BigQuery SQL (and similar language) queries to extract, clean, and interpret massive, connected vehicle datasets in the propulsion systems domain.
• Develop Data Pipelines: Design, build, and own robust data pipelines and workflows using Python, PySpark, and modern data engineering tools to support ML model training and deployment.
• Coordinate Data Creation: Partner with vehicle software teams to define and create new connected vehicle data elements, and support the validation of these new telemetry signals.
• Validate via Calibration Tools: Utilize in-vehicle calibration tools (ATI / ETAS) to collect high-frequency data to validate connected data and verify ML model predictions on key propulsion features and subsystems.
• Synthesize & Communicate Insights: Summarize and present complex machine learning models, statistical analyses, and big data findings in a simplified, visual fashion to both technical and non-technical audiences, including executive leadership.

Connected Vehicle Data Engineer

• 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

• Leverage data and analytics to provide strategic insights
• Develop and implement innovative solutions to optimize business performance
• Build and maintain data pipelines and architectures
• Implement data visualization tools to provide actionable insights
• Collaborate with stakeholders to identify and address data challenges
• Coach and motivate teams to solve complex problems
• Validate outcomes with clients and act on feedback

Senior Data Engineer – Senior Manager

• Provide consultation and data-strategy support for IS initiatives.
• Run EA IS Data Strategy Office Hours and provide guidance on data challenges.
• Partners closely with the EDP team—understand their roadmap and share relevant updates with IS EA architects.
• Design and develop information/data models (enterprise, conceptual, logical, and physical) for Industrial Systems; manage and maintain models in a Power Designer repository.
• Map applications to the conceptual data model, business processes, and business capabilities.
• Support product-team intake: translate use cases into data requirements and provide expert architecture guidance and designs.
• Lead discovery, onboarding, and analysis of sources into Industrial Systems Data Platform (ISDP); build deep understanding of the data landscape and enable integration of diverse sources into ISDP.
• Lead conceptual discussions to understand the data landscape; identify and evaluate candidate data sources; maintain data models and data dictionaries for identified source systems; and develop canonical domain models.
• Profile source-system data; define data-quality requirements, dimensions, and metrics; and guide monitoring, measurement, reporting, and issue remediation.
• Establish data-architecture principles, standards, best practices, and guidelines for the data platform (catalog, lineage, observability, security, interoperability).
• Perform proof-of-concepts and guide product teams in designing and developing solutions using GCP data-engineering technologies and tools.

Data, Information Architect

• 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

• **What You’ll Do **
• As a Senior Databricks Data Engineer at Clarkston you will:
• - Support our solution architects, software developers, data analysts and data scientists on data and analytics initiatives
• - Serve in a lead capacity on client initiatives
• - Ensure optimal data delivery architecture is consistent throughout ongoing projects
• - Be self-directed and comfortable supporting the data needs of multiple teams, systems and products
• - Be excited by the prospect of building our company’s data engineering capabilities to support our next generation of products and data initiatives
• - Lead, grow, and mentor a data engineering team in developing, maintaining, and monitoring data solutions for clients
• - Assist in defining client solutions and support managing their delivery

Senior Data Engineer – Databricks

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• 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

• Collaborate with teams of IT/BI and non-IT personnel to create, manage, maintain and develop enterprise data framework and pipelines.
• Provide hands-on leadership and feedback on projects and production systems.
• Provide training, guidance, and thought leadership to the Data Engineering team.
• Take a leadership role in large-scale corporate projects such as integrations and major customer impacting system upgrades.
• Expand and optimize data pipeline framework across multiple applications and systems.
• Collaborate with the BI team and business analysts to review requirements and translate to ETL design and implementation.
• Understand ETL technical approaches and designs.
• Provide support for existing data pipeline frameworks.
• Diagnose and troubleshoot problems with existing data structures, ETL processes and logic.
• Take a leadership role in setting organizational data management and architecture strategy.
• Manage projects and workload according to FMB standard technology.

• Follow the technical strategy and direction for the Data Platform environment at GFS, including tools, techniques, and processes.
• Help design and build technology solutions across multiple initiatives at the same time.
• Collaborate with others on the team to rapidly prototype and implement technical architectures to deliver insightful solutions.
• Understand the business analytics problems the company faces and help build solutions that solve them.
• Work on a high-performing team focused on results and relationships.
• Performs other duties as assigned.

Intermediate Data Engineer

• As a senior data engineer, you will be responsible for ideation, architecture, design and development of our key data platform components
• Create and maintain essential data platform SDKs and libraries, adhering to industry best practices
• Design and develop connector frameworks and modern connectors to source data from disparate systems both on-prem and cloud
• Design and optimize data storage, processing, and querying performance for large-scale datasets using industry best practices while keeping costs in check
• Design and develop data quality frameworks and processes to ensure the accuracy and reliability of data
• Collaborate with data scientists, analysts, and cross functional teams to design data models, database schemas and data storage solutions
• Proactively identify and contribute towards platform resiliency
• Design and develop observability and data governance frameworks and practices
• Stay up to date with the latest data on engineering trends, technologies, and best practices
• Drive the deployment and release cycles, ensuring a robust and scalable platform
• Partner with AI enablement teams across the organization as well as KION IT Cloud Infrastructure, AI Platform, and security teams to ensure AI/ML capabilities align with IT framework and guidelines within KION
• Partner with Business Transformation Data Management teams to align on master data

• 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

• Implement methods for automation of all parts of the pipeline to minimize labor in development and production.
• Analyze complex data, organizing raw data, and integrating massive datasets from multiple data sources to build analytical domains and reusable data products.
• Work with architects to evaluate and productionalize data pipelines for data ingestion, curation, and consumption.
• Communicate with stakeholders to formulate business problems as technical data requirements, identify and implement technical solutions while ensuring key business drivers are captured in collaboration with product management.
• Develop exceptional analytical data products using both streaming and batch ingestion patterns on Google Cloud Platform with solid data warehouse principles.
• Serve as the Subject Matter Expert in Data Engineering with a focus on GCP native services and other well integrated third-party technologies.

• 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.

• Leveraging data and analytics to provide strategic insights and drive informed decision-making for clients
• Developing and implementing innovative solutions to optimize business performance and enhance competitive advantage (solution optimization and roadmap development)
• Utilizing advanced analytics techniques to support client operations and strategic goals (AWS data engineering and Redshift)
• Building and maintaining data pipelines and architectures to support business intelligence efforts (DataStage, ETL)
• Implementing data visualization tools to provide actionable insights and improve client outcomes (SQL Server, DB2)
• Collaborating with stakeholders to identify and address data challenges and opportunities (Managed services, and SLAs)
• Coaching and motivating teams to solve complex problems and deliver quality results
• Applying systems thinking to identify underlying problems and opportunities within client projects (workload orchestration and scheduling)
• Validating outcomes with clients and acting on feedback to refine solutions (CSAT and performance tuning)
• Directing teams through ambiguous and challenging situations with composure and sound judgment

• Developing and implementing data-driven solutions to optimize business performance and enhance competitive advantage (data engineering, ETL)
• Leveraging advanced analytics techniques to provide strategic insights and drive informed decision-making for clients (SQL optimization)
• Building and maintaining data pipelines to support efficient data collection and analysis (DataStage)
• Utilizing business intelligence tools such as Oracle Business Intelligence and QlikView to create comprehensive reports and dashboards (DB2, SQL Server)
• Collaborating with team members to gather and analyze data from various sources to discern patterns and trends (star schemas, dimensions)
• Applying data architecture development skills to design and maintain robust database management systems (snowflake models)
• Engaging in continuous process improvement to enhance data quality and integrity
• Supporting client engagement by actively listening and responding to diverse perspectives and needs (regulated environments)
• Participating in the development of business cases and market development strategies through data analysis and interpretation
• Adapting to a fast-paced environment and taking ownership of tasks to consistently deliver quality work (job scheduling, UNIX)

Data Engineer jobs in Missouri

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Vehicle Dynamics Simulation Engineer

Data Architect

Connected Vehicle Data Engineer

Senior Data Engineer

Senior Data Engineer – Senior Manager

Data, Information Architect

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer – Databricks

Data Engineer

Staff Data Engineer

Senior Data Engineer

Intermediate Data Engineer

Senior Data Engineer

Staff Software Engineer – DC Tech Lead

Senior Data Engineer

Staff Data Engineer

Data Architect

Senior Data Engineer – Senior Manager

Data Engineer – Experienced Associate