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

• Seek candidates for the role of a Principal Data Links & Communication Engineer
• Requires Link 22 and tactical data links engineering experience
• Advise and assist the program Chief Engineer, IPT Leads, and PMs on performance, schedule, and cost issues
• Provide knowledge and experience in the development, integration, testing, and deployment of Air Force systems and associated data links
• Interface with the Federal, DoD, USAF, USA, and Joint communities to ensure data link communication capability compliance

Principal Data Link, Communications Engineer

• 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

• Ensure data pipelines comply with security policies and regulatory requirements (e.g., HIPAA, NIST, SOC 2)
• Develop testing plans to ensure quality of implementation, including deployment validation, data quality checks, reconciliation, and repeatable testing of data pipeline changes.
• Determine and perform complex configuration changes to meet business and information security requirements, including Microsoft Fabric workspaces, data pipelines, connections, service principals, secrets, and environment-specific configuration.
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues.
• Provide technical support to business and technology associates in risk assessments and implementation of appropriate information security procedures, standards and technologies.
• Represent major upgrades and reconfigurations in change control; design and analyze vendor and platform services meeting business and information security requirements, including governed promotion across development, test, and production environments.
• Provide system, data platform, and security architecture support for information security technologies and data pipeline capabilities.
• Represent infrastructure security support in significant projects and perform the most complex operations and administration tasks.
• Respond to level 3 & 4 change and problem requests without supervision; Lead level 1 & 2 incident recoveries and root cause analysis.
• Coordinate and prepare the reporting of data security events and incidents.
• Perform capacity analysis; recommend and implement capacity increases for data platform performance, scalability, and reliability.
• Serve as the technical escalation for results of preventative maintenance routines; Supervise preventative maintenance; Leads lights on initiatives to consolidate equipment and/or implement business relocations.

Data Engineer – Secure Data Pipeline

• 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 maintain scalable and efficient data pipelines and ETL processes
• Collaborate with analysts and stakeholders to gather requirements and implement data solutions that meet business needs
• Proposes & designs data solutions to address complex business needs
• Prepare technical and procedural documentation required
• Participate in code reviews & testing
• Develop and manage data integrations between various systems, ensuring seamless data flow and consistency
• Optimize database and query performance to support analytical workloads
• Monitor and troubleshoot data workflows to ensure data quality and integrity

• Lead the strategy and execution of complex and large Data and Analytics portfolio.
• Develop and implement a strategic roadmap to modernize legacy data and analytics ecosystems using Cloud and AI.
• Ensure data architecture and solutions align with enterprise-wide standards for Data, AI and Analytics.
• AI Data Engineering leader responsible for Implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions.
• Design, build and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• Oversee the design, development, and maintenance of lakehouse architectures, data pipelines, data warehouses, data lakes and reporting systems.
• Mentor and lead a high-performing team including Directors, Business data analysts, Data engineers and Release train engineers.
• Identify and champion developer productivity improvements across the end-to-end data management lifecycle.

Director, Data Architect

• Define and champion the end-to-end architecture for the Databricks data science and AI platform across a global topology — ensuring scalability, security, cost efficiency, resilience, and compliance with regional data sovereignty laws.
• Establish reference architectures, patterns, and blueprints that encourage enterprise-wide reuse and standardization.
• Lead the design and adoption of Unity Catalog as the central governance plane—defining catalog hierarchies, fine-grained (row/column) access, dynamic data masking, lineage, and classification.
• Partner with infrastructure, security, and compliance teams to enforce least-privileged access, responsible AI, and regulatory requirements including data sovereignty across US, EMEA, and APAC.
• Architect robust, low-duplication integration between Databricks and the enterprise data platform (Snowflake) using open formats such as Apache Iceberg and Unity Catalog federation.
• Enable seamless bi-directional data access and feature sharing so teams can choose the right tool without paying a tax to deploy across environments.
• Partner directly with actuarial, data science, and analytics teams to understand their workflows and build frameworks, templates, IDE/notebook experiences, and documentation that let them independently develop, train, and deploy models and AI agents.
• Mature the ModelOps strategy spanning model lifecycle, governance, versioning, and deployment at scale using MLflow, Model Serving, and the AI Agent Framework.
• Optimize compute, clusters, and jobs (classic and serverless) for performance, reliability, and cost.
• Establish platform and solution-level observability, tagging coverage, chargeback/show back, and self-service cost and usage analytics so teams can consume responsibly with predictable spend.
• Serve as a trusted advisor and enablement partner to data science and business teams—running discovery workshops, proofs-of-concept, and technology evaluations.
• Curate a community of practice, promote sanctioned patterns, and continuously bring contemporary Databricks best practices to complex data and AI challenges.
• Mentor and upskill team members, promote knowledge sharing, and build high-performing, cross-functional teams focused on excellence in data and AI.

Information Management Data Architect

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
• Build and optimize large-scale data processing solutions using Python, PySpark, and Java.
• Develop data ingestion frameworks for structured and unstructured data sources.
• Integrate data from various systems through REST APIs, streaming platforms, and batch processing.
• Work with large datasets in distributed computing environments.
• Develop and support real-time and batch data processing solutions using Kafka and Hadoop ecosystem technologies.
• Implement scalable streaming data pipelines and event-driven architectures.
• Monitor and optimize data workflows for performance and reliability.
• Write complex SQL queries for data extraction, transformation, and analysis.
• Design and optimize database schemas and data models.
• Ensure data quality, consistency, and governance standards are maintained.
• Participate in Agile ceremonies including sprint planning, daily stand-ups, backlog grooming, and retrospectives.
• Use Jira for project tracking, issue management, and sprint execution.
• Collaborate with Data Architects, Data Scientists, Business Analysts, and Application Development teams.
• Work independently as well as within a collaborative team environment.
• Troubleshoot and resolve data-related issues across environments.
• Perform root cause analysis and implement long-term solutions.
• Support production deployments and ongoing maintenance activities.

• 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

• Define and own the target-state master data architecture for the SAP ECC to S/4HANA Cloud migration
• Serve as design authority for the master data workstream — setting data models, conversion standards, and migration design principles aligned to SAP Activate
• Provide architectural guidance in Fit-to-Standard workshops for data domains
• Analyze the SAP Simplification List to identify deprecated objects, mandatory conversions, and architectural changes
• Architect master data solutions adhering to Clean Core / ABAP Cloud extensibility frameworks
• Design modern data exposure and consumption models leveraging Core Data Services (CDS) Views and the SAP Business Technology Platform (BTP) ecosystem
• Lead the architectural design and structural alignment of Finance master data for S/4HANA
• Establish and maintain standard data models for Business Partner, Material, and Finance domains to ensure cross-functional data integrity
• Select and architect the data profiling and automated remediation tooling stack
• Partner with business data stewards to define data quality KPIs and validation standards

Lead Architect – Master Data

• Define and execute the strategic roadmap for FOCUS’s enterprise data engineering platform, including data pipelines, ETL/ELT processes, data lakes, data warehouses, and integration frameworks
• Establish architectural standards for data ingestion, transformation, storage, and access that support analytics, reporting, and AI/ML use cases
• Partner with business and technology leaders to prioritize data platform investments and ensure alignment with enterprise strategy and regulatory requirements
• Provide executive-level reporting on platform health, data quality, and delivery outcomes
• Own the lifecycle management of FOCUS’s business application portfolio, including policy administration, agency management, billing, claims, and document management systems
• Oversee vendor relationships, licensing, contract renewals, and SLA performance for all business applications
• Lead application rationalization, modernization, and integration efforts to improve operational efficiency and reduce technical debt
• Partner with business stakeholders to evaluate new application capabilities and drive adoption of technology solutions that improve productivity and support FOCUS’s growth strategy
• Lead, grow, and develop a high-performing team of data engineers, integration developers, and business application specialists
• Drive talent strategy, succession planning, performance management, and professional development across the function
• Establish engineering standards, development practices, and operational KPIs for the team
• Directly manages Data Engineering team leads/managers and Business Applications managers, and may directly manage senior individual contributors (e.g., Principal Data Engineers, Senior Application Analysts)

Director, Data Engineering – Business Applications

• 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.

• 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

• Assists in developing, supporting and implementing data solutions for multiple applications to meet business objectives
• Leverages technical knowledge and industry experience to design, build and maintain technology solutions
• Performs data requirement analysis and data preparation process development for targeted data solutions
• Assists in designing and building data service infrastructure on multiple data platforms according to the workflow
• Participates in developing and implementing data solutions ensuring scalability, availability and maintainability
• Consults on data migration and transformation to ensure accuracy and security of data solutions

Data Engineer Associate – Data & Analytics Technology

• 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

• Develop and optimize ETL processes to support data integration and transformation requirements.
• Collaborate with business and technical teams to gather, analyze, and translate requirements into data solutions.
• Support business intelligence and analytics initiatives by ensuring data availability, quality, and consistency.
• Ensure solutions align with enterprise architecture standards, security requirements, and best practices.
• Conduct risk assessments and identify opportunities to improve data management processes.

• 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.

Data Engineer jobs in Ohio

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Senior Data Engineer

Principal Data Link, Communications Engineer

Senior Software Engineer, Data

Data Engineer – Secure Data Pipeline

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Director, Data Architect

Information Management Data Architect

Data Engineer

Data Engineer

Lead Architect – Master Data

Director, Data Engineering – Business Applications

Senior Data Engineer

Senior Data Engineer

Data Engineer Associate – Data & Analytics Technology

Data Engineer – Oracle, MongoDB DBA Specialist

Senior Enterprise Architect, Enterprise Data Architect

Data Architect

Data Architect

Director of Engineering – Data Platform