Data Engineer

• Design, build, and productionize reliable, scalable, and secure data pipelines and data products in Azure Databricks that support AI, analytics, and operational use cases.
• Lead the transformation of raw data from multiple source systems into trusted, well-structured datasets suitable for downstream analytics, model development, and AI enablement.
• Drive improvements in internal processes, delivery patterns, and technical solutions that support broader functional strategy and increase team efficiency.
• Solve complex and non-standard data engineering problems using advanced analytical and problem-solving techniques, with strong ownership and sound technical judgment.
• Partner closely with data scientists, analysts, software engineers, product stakeholders, and business teams to ensure data is accessible, trustworthy, and aligned to business outcomes.
• Support AI and data science use cases by enabling high-quality feature-ready data, experimentation workflows, and scalable patterns for model development and deployment.
• Contribute to technical direction within the team, influence key engineering decisions in their area, and act as a go-to resource for peers and less experienced engineers.
• Mentor team members through technical guidance, knowledge sharing, and strong engineering practices consistent with Level 7 expectations.

Senior Data Engineer

• Lead the end-to-end design, development, deployment, and support of scalable, production-grade data integration and engineering solutions
• Create and maintain technical designs and documentation aligned with business requirements, data architecture, and enterprise standards
• Collaborate with technology leadership to evaluate and implement new tools, technologies, and best practices that improve platform performance and user experience
• Partner with Product Management and business stakeholders to deliver data solutions that meet business objectives and design requirements
• Build strong cross-functional relationships, effectively communicating technical solutions and driving stakeholder alignment
• Mentor junior engineers, guide architectural decisions, and balance technical tradeoffs to deliver reliable and intuitive data products
• Drive operational excellence through monitoring, optimization, troubleshooting, and ongoing support of data platforms and services

Lead Data Engineer

• Design, develop and refine performance metrics and advisor service metrics for OnStar Blue Button IVA project
• Build and maintain reporting and analytics including dashboards and recurring reports that visualize advisor service usage and system availability across OnStar services such as Turn-By-Turn Navigation and Remote Unlocks
• Utilize tools such Databricks, SQL and PowerBI to analyze backend data sources and provide performance insights
• Collaborate with IT and operations to ensure reports align with business goals and SLA tracking
• Understand and document system workflows, associated back-end interactions, and eco-system of the OnStar Guardian application
• Investigate end-to-end system performance and customer experience issues and apply sound technical judgment to data to resolve them
• Identify system performance gaps, build and execute improvement plans
• Ensure adequate system requirements are documented
• Design, develop, and implement desktop applications and processes to automate routine tasks as necessary to facilitate analysis activities.

Data Engineer – Customer Experience

• Model Development: Design and implement advanced machine learning models for predictive maintenance, anomaly detection, and computer vision-based quality control.
• End-to-End Pipeline Construction: Architect data pipelines from ingestion (sensor data, PLC logs) to model deployment and monitoring using GCP and Python.
• Statistical Analysis: Apply rigorous statistical methods to identify patterns in manufacturing data that correlate with vehicle quality or equipment downtime.
• Cross-Functional Collaboration: Partner with Product and Engineering teams to translate manufacturing pain points into technical requirements and deliver user-centric data products.
• Technical Leadership: Act as a subject matter expert within ATP, conducting code reviews, mentoring junior scientists, and staying at the forefront of AI/ML research in the industrial space.
• Scalability: Ensure models are optimized for production environments, moving from localized pilots to global plant-wide deployments.
• Data Strategy: Work with data engineering to improve data collection protocols and sensor telemetry quality from the plant floor.

• Design, build, and maintain scalable cloud-native data pipelines—including ETL/ELT, event-driven, and serverless patterns using Azure Functions and related services—that ingest, transform, and load data from Continental’s diverse source systems including ERP, POS, HRIS, and finance platforms into a centralized lake or data warehouse.
• Develop and maintain data models within Microsoft Fabric and Azure that support analytics, business intelligence, and emerging AI use cases, ensuring data is structured for both performance and usability.
• Integrate data from disparate systems across Continental’s business units into a unified enterprise data model, resolving inconsistencies and establishing a single source of truth for key business metrics.
• Implement and enforce data quality, validation, and monitoring practices that ensure downstream consumers—analysts, dashboards, and AI models—can trust the data they are working with.
• Collaborate closely with the Business Analyst and IT AI & Data Manager to understand reporting and analytics requirements, translating them into efficient, well-documented data solutions.
• Design and implement AI-augmented data engineering solutions—including AI agents, automated data quality processes, and intelligent pipeline components—that drive efficiency across Continental’s data platform. This is not a support role for AI; we’re looking for someone who can actively build and deploy AI-driven capabilities within the data engineering workflow.
• Build and optimize semantic models and datasets that power PowerBI dashboards and reports, working with analysts to ensure visualizations are backed by performant, accurate data.
• Contribute to data governance efforts by maintaining data dictionaries, pipeline documentation, and lineage tracking so the team can understand, trust, and build on the work being done.
• Monitor pipeline health, troubleshoot data failures, and proactively address issues before they impact reporting or downstream business processes.
• Stay current on developments in cloud data engineering, AI tooling, and the Microsoft data ecosystem, bringing new ideas and approaches to the team as the platform continues to mature.
• Support M&A and new business onboarding by connecting new source systems into existing pipelines and data models with minimal disruption to existing operations.

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Job Title: Sr. Data Engineer - MS Fabric, ETL/ELT, Data Modelling, and Cloud platforms
• Location: Lansing, MI (Onsite-Hybrid)
• Overall, 10 + years of experience and Azure Certification is MUST.
• We are looking for a hands-on Data Engineer with strong experience in MS Fabric, ETL/ELT, Data Modelling, and Cloud platforms.
• We are looking for someone who can work independently.
• Candidates with experience working in or leading junior teams would be a good fit.
• We mainly need someone who can communicate well, present their experience confidently, and perform strongly.

• Define and maintain the technical vision, architecture principles, and engineering strategy for the Solution Data Repository.
• Lead the design and development of an enterprise ontology to inform the technical architecture.
• Drive practical interoperability between catalog/SDF sources and downstream tools.
• Implement and enforce engineering lifecycle standards, code quality practices, release discipline, and operational readiness criteria.

Enterprise Data Architect – Solutioning Suite

• Collaborate with senior data engineers to translate complex business requirements and ambiguous problem statements into clear, robust, and scalable technical designs and data models (e.g., dimensional modeling, star schemas), and independently drive the implementation of these designs.
• Design, build, and deploy high-volume data transformation logic using highly optimized PySpark.
• Contribute significantly to the design and improvement of CI/CD pipelines in Azure DevOps/Git.
• Deeply understand and connect to various source systems, demonstrating proficiency in managing data persistence and query performance across diverse technologies like SQL Server, Neo4j, and CosmosDB.
• Proactively implement and maintain advanced data quality frameworks (e.g., Delta Live Tables, Great Expectations) and monitoring solutions to ensure data reliability for mission-critical applications.
• Serve as a go-to technical resource for peers, conducting technical code reviews and informally mentoring Associate Data Engineers on PySpark and Databricks best practices.

• Spearhead the design, development, and maintenance of scalable data ingestion and curation pipelines from diverse sources.
• Ensure data is standardized, high-quality, and optimized for analytical use.
• Leverage cutting-edge tools and technologies, including Python, SQL, and DBT/Dataform, to build robust and efficient data pipelines.
• Utilize your full-stack skills to contribute to seamless end-to-end development, ensuring smooth and reliable data flow from source to insight.
• Leverage your deep expertise in GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Functions, etc.) to build and manage data platforms that not only meet but exceed business needs and expectations.
• Implement and manage robust data governance policies, access controls, and security best practices, fully utilizing GCP's native security features to protect sensitive data.
• Employ Astronomer and Terraform for efficient data workflow management and cloud infrastructure provisioning, championing best practices in Infrastructure as Code (IaC).
• Continuously monitor and improve the performance, scalability, and efficiency of data pipelines and storage solutions, ensuring optimal resource utilization and cost-effectiveness.
• Collaborate effectively with data architects, application architects, service owners, and cross-functional teams to define and promote best practices, design patterns, and frameworks for cloud data engineering.
• Proactively automate data platform processes to enhance reliability, improve data quality, minimize manual intervention, and drive operational efficiency.
• Clearly and transparently communicate complex technical decisions to both technical and non-technical stakeholders, fostering understanding and alignment.
• Stay ahead of the curve by continuously learning about industry trends and emerging technologies, proactively identifying opportunities to improve our data platform and enhance our capabilities.
• Translate complex business requirements into optimized data asset designs and efficient code, ensuring that our data solutions directly contribute to business goals.
• Develop comprehensive documentation for data engineering processes, promoting knowledge sharing, facilitating collaboration, and ensuring long-term system maintainability.

Full Stack Data Engineer

• Apply engineering principles and methodologies to solve complex technical challenges
• Troubleshoot technical issues and develop effective solutions in a timely manner
• Stay up to date with the latest advancements in relevant technologies
• Collect, analyze, and interpret large datasets using Excel and other analytical tools
• Develop and maintain insightful reports and dashboards to track key performance indicators (KPIs) and identify trends
• Use data-driven insights to make recommendations for process improvements, cost reduction, and efficiency gains
• Communicate technical information clearly and effectively to both technical and non-technical audiences
• Collaborate with cross-functional teams (e.g., sales, marketing, operations) to achieve common goals
• Present technical findings and recommendations to management in a concise and persuasive manner
• Act as a technical resource and mentor to junior engineers
• Understand the business implications of engineering decisions and contribute to strategic planning
• Identify opportunities to leverage technology to improve business processes and gain a competitive advantage
• Participate in cost-benefit analyses and ROI calculations for engineering projects
• Stay informed about industry trends and market dynamics
• Develop project plans, track progress, and identify and mitigate risks

Data Architecture, Information Strategy Engineer

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Define and execute the vision and roadmap for products in the Data Platform Engineering ensuring alignment with Ford’s broader data and analytics strategy.
• Lead user discovery and research across multiple personas (business associates, plant engineers, analysts, developers) to ensure solutions address real pain points.
• Translate user needs into epics, PRDs, and evaluation benchmarks; prioritize features and enhancements using impact-driven frameworks.
• Drive adoption and continuous improvement of solutions, enhancing data governance, operational efficiency, and regulatory compliance.
• Define KPIs and success metrics to measure adoption, discoverability, and quality.
• Lead the development of a Data Catalog that enables seamless search, discovery, and governance of enterprise-wide data assets.
• Develop metadata governance models, ensuring metadata standardization, data stewardship, and AI-driven metadata automation.
• Partner with engineering, data science, and platform teams to deliver scalable and secure solutions across cloud and enterprise ecosystems.
• Communicate product strategy and progress to senior leadership and stakeholders, influencing adoption and organizational alignment.
• Build and maintain success metrics around adoption, accuracy, usability, and performance to guide continuous improvement.

Data Platform Product Manager

• Define the Vision for AI Readiness: Establish and document the enterprise standards for AI-Ready data, including semantic integrity, automated metadata tagging, high-fidelity pipelines, etc.
• Drive Platform Evolution & Modernization: Proactively identify and champion the adoption of next-generation technologies and cloud-native patterns. Act as a strategic catalyst for transitioning the EDP toward higher-maturity operating models, ensuring Ford remains at the cutting edge of data and AI innovation.
• Design and Build for Agentic Intelligence: Work cross-functionally within EDP to design and build data for AI agents to interpret without human intervention while ensuring consistency and context across the enterprise.
• Collaboration & Leadership: Lead and mentor an organization of Data Architects and Engineers. Working within the Data Platform and Engineering team to ensure solutions are scalable and executable.
• Privacy & Compliance by Design: Ensure development patterns enable Ford’s rigorous commitment to data security and global privacy regulations.

Director, Data Engineering, Architecture

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• 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.

Manager, Data Engineering

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• Contribute to overall data standards, information interoperability, data quality, and data availability.
• Autonomously perform Quality Assurance and resolve data quality issues.
• Evaluate, explore, and select the right data platform technologies to meet analytics requirements.
• Model data, create data flow diagrams, load, and perform transformation.
• Independently drive alignment and delivery of data transformations and data products to meet business objectives.
• Build product and align the best coding and process practices that improve efficiency, speed, stability, system resources and capabilities.
• Lead in translating business requirements to build scalable, sustainable, efficient, coded processes.
• Productionize coded processes and contribute to overall customer/end-user experience strategy and vision.
• Support scalable growth, to accelerate delivery of business value.
• Drive the data strategy and align it with continuously changing business landscapes utilizing their background in data design, data architecture, and data modelling (both transactional and analytical).

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