Data Engineer

• Design and manage Feed the Children’s modern cloud data infrastructure, with ownership of Microsoft Fabric and Azure.
• Lead the stand up and evolution of Feed the Children’s Fabric platform and serve as its primary Product Owner.
• Collaborate closely with ERP and CRM teams, and coordinate with Data Governance leadership.
• Ensure seamless integration of data and data flows from various sources, including ERP systems, CRM systems, DHIS2, external datasets and APIs, and other business applications.
• Implement processes and tools to ensure high data quality and consistency across the organization.

Data Architect

• 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

• 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

• 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

• Design, document, deploy, and manage Data Center Network solutions through all phases of lifecycle management for Cisco ACI & Nexus equipment and Radware Load Balancers deployed at enterprise hosting locations and local facilities.
• Support infrastructure and deployment projects across multiple regions and locations.
• Partner with hardware, software, and vendor teams on the development of data center solutions.
• Provide project participation, advice, and counsel to IT personnel, management, and project teams on various aspects of enterprise Data Center Network hardware and software.
• Establish and implement global security and operational best practices and adhere to IT Controls.
• Adhere to IT Service Management processes, instruction, and procedural documentation.
• Work with application support teams/customer/operations management to develop plans to minimize the technical and/or business impact of network environment changes.
• Participate in an on-call rotation to support the global datacenter network infrastructure 24x7.
• Monitor, troubleshoot, and resolve complex problems within the enterprise Data Center networks.

Senior Data Center Network 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

• 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.

• Serving as a key technical leader within the Data and Analytics organization
• Translate the Sr. Director’s enterprise data strategy into actionable technical roadmaps and architectural patterns
• Guide the development and lifecycle management of data solutions across reporting, predictive intelligence, and data science domains
• Lead the analysis and design of complex, high-impact projects, products, and shared infrastructure services
• Proactively identify and socialize opportunities to simplify or rationalize the data platform
• Supervise a team of 1–2 Data Architects
• Define and maintain architectural governance, data modeling standards, and security protocols
• Partner with the VP of Business Intelligence and IT Data Engineering

Senior IT Data Architect

• Designing, building, and maintaining the organization’s enterprise data architecture, data pipelines, and integration solutions.
• Transforming raw data into trusted, accessible, and actionable information that supports business operations, reporting, advanced analytics, artificial intelligence (AI), and strategic decision-making.
• Design, develop, and maintain enterprise data pipelines and integration processes.
• Build scalable data solutions supporting reporting, analytics, and AI initiatives.
• Design and implement data warehouse, data lake, and lakehouse architectures.
• Develop and optimize ETL/ELT processes.
• Implement data quality controls, metadata management, and governance standards.
• Support cloud-based and hybrid data platforms.
• Collaborate with analysts, scientists, and business stakeholders.
• Mentor junior engineers and establish engineering best practices.

• Serve as a subject matter expert on HOPE’s data systems, business processes, workflows, and integration points.
• Design and implement robust data ingestion pipelines and ETL solutions for internal and external data sources.
• Manage all components of the Microsoft Azure ecosystem, including compute, storage, networking, databases, analytics, and governance.
• Ensure data loads and pipelines run reliably, with minimal impact on reporting and downstream systems.
• Lead data migration and conversion efforts for new software and system implementations.
• Lead, mentor, and develop a team of data professionals, fostering a culture of innovation, quality, and continuous improvement.
• Provide guidance on best practices for data management, data quality, analytics, and pipeline development.
• Collaborate with technology leaders, business partners, and cross‑functional teams to align data strategy and evaluate emerging tools and technologies.
• Partner with HOPE’s Learning & Development team to deliver end‑user training on reporting and data literacy.
• Oversee the use of statistical, analytical, and AI/ML methods to uncover insights, automate processes, and solve business challenges.
• Design and manage processes for data intake, validation, mining, engineering, modeling, visualization, and communication.
• Build Power BI dashboards and automated reporting solutions that address HOPE’s unique business needs.
• Recommend smart, data‑driven solutions that support organizational goals and operational efficiency.
• Ensure departmental milestones and goals are met within approved budgets.
• Partner with the Information Technology team to ensure proactive monitoring and timely system upgrades.
• Manage day‑to‑day staff performance, including timekeeping, capacity planning, and performance management.
• Perform other duties as assigned.

Manager, Data Architecture

• Provide strategic vision and technical leadership for both the Data Platform and transformation processes.
• Collaborate with systems engineering, product, and business leaders to align platform development with business objectives.
• Drive the adoption of best practices in data engineering, and software development.
• Lead the development and evolution of our next-generation, storage mechanisms for our large data sets (Data Lake, Data Warehouse or Operational Data Store), enabling seamless data ingestion, processing, and analysis.
• Architect and implement scalable data pipelines, optimizing for latency, cost, and throughput.
• Define and enforce data management practices, including data modeling, ETL, and data warehousing principles.
• Liaise with leaders from individual data domains to understand business priorities and drive data asset efforts from inception to release management.
• Lead and manage the Database Administration function, providing guidance to ensure high availability, scalability, and performance.
• Monitor data transformation efforts for accuracy, reliability and performance.

Director, Data Architecture – Engineering

• 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.

• Design and tune SQL and Snowflake models.
• Build and orchestrate engineering pipelines that move data between systems on AWS.
• Interrogate data and turn it into Power BI dashboards.
• Use modern AI tooling to improve the platform.

Junior Full Stack Data Engineer

• 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

• 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

• Lives out Lifeway’s mission and values, showing deep commitment to Kingdom work
• Define and maintain the enterprise data architecture roadmap, aligning data strategy with long-term business priorities and technology goals
• Lead the design and evolution of data platforms including data warehouse and lake-house architecture
• Develop and champion a Data Mesh strategy enabling domain-oriented data ownership, self-serve data infrastructure, and federated governance
• Architect and oversee enterprise data integration patterns including batch ETL, real-time streaming, and event-driven architectures
• Establish standards for data pipelines, transformation logic, lineage tracking, and integration frameworks across on-premises and cloud environments
• Lead efforts to make the organization's data AI-ready to support machine learning and AI applications
• Drive adoption of data contracts and data product thinking to improve reliability and trust in data assets
• Lead the design of Master Data Management integrations and provide technical leadership across MDM initiatives
• Act as a strategic advisor on data initiatives across business units, ensuring architectural soundness and alignment with enterprise standards
• Provide technical mentorship and architectural guidance to data engineers, BI developers, and MDM Software Engineers

Data Engineer jobs in Tennessee

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Data Architect

Senior Data Engineer

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Data Engineer

Staff Data Engineer

Senior Data Center Network Engineer

Data Engineer II

Senior Data Engineer

Senior IT Data Architect

Senior Data Engineer

Manager, Data Architecture

Director, Data Architecture – Engineering

Staff Data Engineer

Data Architect

Junior Full Stack Data Engineer

Director of Engineering – Data Platform

Staff Data Engineer

Manager, Data Engineering

Principal Data Architect