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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Platform Manager - **Company:** Johnson & Johnson - **Location:** Raynham, MA, United States - **Experience:** Expert - **Salary:** $102,000.0 - $177,100.0 - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Business Analytics Applications, Automation of Tests, Microsoft Azure, Cloud Database, Software Quality, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Cursor, Dimensional Modeling, Distributed Data Store, Apache Hive, Python (Programming Language), Power BI, Software Tools, Cloud Services, Azure Machine Learning, Software Deployment, SQL Databases, Data Ingestion, Azure Data Factory, GitHub Copilot, Claude Code, Change Data Capture, Data Lakes, AI Platforms, Pyspark, Information Technology, AWS Glue, Data Management, Software Coding, Meditech, Data Pipelines, Serverless Computing, Databricks - **Published:** October 20, 2026 - **Apply:** https://www.careers.jnj.com/en/jobs/r-103858/manager-data-platform/ ## About the Role Education: A minimum of a Bachelor's degree and/or equivalent University degree is required; a focused degree in Computer Science, Engineering, Applied Mathematics, Information Systems or a related field is preferred. Masters degree preferred Experience and Skills: Required: * Minimum of six (6) years of relevant professional work experience * Multifaceted experience in data engineering or data platforms, including in technical leadership, people management, or platform ownership roles. * Validated experience in leading the design, implementation, or modernization of a data platform or lakehouse and converting strategy into an executable roadmap. * Strong hands-on expertise with Databricks, PySpark, Spark SQL, Python, SQL, Delta Lake, and cloud data services, with Azure and/or AWS experience. * Demonstrated experience designing reusable ingestion and data pipeline frameworks, including orchestration and integration with dbt, Apache Airflow, Azure Data Factory, AWS Glue, or comparable tools. * Strong knowledge of data architecture and modeling, including dimensional modeling, star and snowflake schemas. * Experience defining and implementing automated testing strategies for data engineering code and pipelines, including unit, integration, regression, reconciliation, data-quality testing, and CI/CD integration. * Experience establishing engineering governance, coding standards, release controls, documentation expectations, architecture reviews, and production-readiness practices for distributed data teams. * Experience implementing data observability across pipelines and products, including monitoring, lineage, reliability, alerting, and service-level management. * Experience leading distributed teams and governing external vendors or managed-service partners against architecture standards, quality expectations, achievements, and business outcomes. * Ability to communicate sophisticated platform concepts to technical and business collaborators and influence decisions in a matrixed organization. Preferred: * Experience with Fivetran or similar managed data integration and change-data-capture platforms. * Experience building a data platform that supports federated business teams, self-service analytics, certified data products, and AI-ready consumption patterns. * Familiarity with end-to-end supply chain domains such as planning, manufacturing, procurement, quality, and distribution within MedTech or another regulated industry. * Knowledge of enterprise systems of record such as ERP, MES, PLM, and planning platforms, including experience modernizing fragmented or legacy data environments. * Experience integrating a data platform with Power BI, semantic models, data marketplaces or catalogs, and AI/ML platforms. * High-level understanding of web and digital application architecture, APIs, integration patterns, and vendor-led application delivery. * Experience using AI-assisted engineering tools such as GitHub Copilot, Cursor, Claude Code, or similar tools to accelerate development, automate repetitive work, and improve engineering quality. * Experience supporting Agile product teams, mentoring engineers, and building reusable platform capabilities embraced across multiple business functions. ## Description As a Data Platform Manager, you will own the strategy, architecture, engineering standards, and roadmap for the modern data platform that enables trusted data products, digital applications, analytics, and AI across Orthopedics. You will lead the Data Foundation initiative to overhaul the current data ecosystem and establish a scalable lakehouse using Databricks, cloud services, and medallion architecture. You will create reusable ingestion, transformation, testing, governance, and observability capabilities that allow teams to onboard data and deliver business-ready products consistently and efficiently. You will also provide technical leadership for business separation activities, ensuring platform continuity and alignment with future-state interpersonal needs. This role combines strategic platform ownership with hands-on technical leadership, people and vendor management, and multi-functional partnership. If you are energized by building reusable platform capabilities and turning strategy into measurable business outcomes, this is an opportunity to make a substantial impact., * Own and implement the Data Platform and Data Foundation strategy, target architecture, roadmap, engineering standards, and measurable outcomes. * Lead the design and evolution of a scalable Databricks lakehouse using medallion architecture, reusable data products, and cloud-native services. * Design, build, and scale reusable, metadata-driven data ingestion and data pipeline frameworks, including batch and streaming patterns, source onboarding, transformation, orchestration, schema evolution, auditability, and embedded data-quality controls. * Establish platform engineering standards for data modeling, code quality, automated testing, CI/CD, documentation, security, performance, cost efficiency, and production readiness. * Design and implement the data engineering governance framework in partnership with Data Governance, embedding ownership, business definitions, policy controls, architecture reviews, data quality, certification, lineage, and traceability into engineering workflows and data products. * Implement end-to-end data observability and operational governance, including monitoring, reliability, service-level expectations, incident prevention, and lifecycle management. * Enable analytics and AI teams with governed, reusable, and AI-ready data while maintaining alignment with the Enterprise AI Platform and business digital-product teams. * Provide technical governance and architectural oversight for business separation and transition activities, protecting continuity while enabling independent future-state platforms. * Translate sophisticated business and digital-product needs into platform capabilities, manageable work packages, delivery roadmaps, and outcome-based execution plans. * Lead internal engineers and delivery partners; set technical direction, review solution quality, dependencies and risks, and build sustainable internal capability.