Manager Data Platform

Johnson & Johnson•Mooresville, MA

3d•Hybrid

About The Position

DePuy Synthes is seeking a Data Platform Manager to own the strategy, architecture, engineering standards, and roadmap for a modern data platform supporting trusted data products, digital applications, analytics, and AI across Orthopedics. The role will lead the Data Foundation initiative to modernize the data ecosystem and establish a scalable lakehouse using Databricks, cloud services, and medallion architecture. It will develop reusable capabilities for ingestion, transformation, testing, governance, and observability so teams can onboard data and deliver business-ready products consistently and efficiently. The position combines strategic platform ownership with hands-on technical leadership, people and vendor management, and cross-functional partnership. It also provides technical leadership for business separation activities, supporting platform continuity and alignment with future-state needs. The role is hybrid, with onsite work required 1 to 3 days per week, and may require up to 20% domestic and international travel.

Requirements

Bachelor’s degree and/or equivalent university degree; a focused degree in Computer Science, Engineering, Applied Mathematics, Information Systems, or a related field is preferred.
At least 6 years of relevant professional work experience.
Multifaceted data engineering or data platform experience, including technical leadership, people management, or platform ownership.
Validated experience 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 schemas, and snowflake schemas.
Experience defining and implementing automated testing strategies for data engineering code and pipelines, including unit, integration, regression, reconciliation, and data-quality testing, with 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.

Nice To Haves

Master’s degree 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.

Responsibilities

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 pipeline frameworks covering 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 a data engineering governance framework 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, AI-ready data while aligning 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 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.

Benefits

Consolidated retirement plan (pension) and savings plan (401(k)), subject to plan terms.
Eligibility to participate in the company’s long-term incentive program.
Vacation: 120 hours per calendar year, subject to policy and hire date.
Sick time: 40 hours per calendar year, with different stated allocations for Colorado and Washington residents.
Holiday pay, including floating holidays: 13 days per calendar year.
Work, Personal and Family Time: up to 40 hours per calendar year.
Parental Leave: 480 hours within one year of a child’s birth, adoption, or foster care placement.
Bereavement Leave: 240 hours for an immediate family member and 40 hours for an extended family member per calendar year.
Caregiver Leave: 80 hours in a 52-week rolling period.
Volunteer Leave: 32 hours per calendar year.
Military Spouse Time-Off: 80 hours per calendar year.