Staff Data Engineer/Surgical Robotics - OTTAVA
Johnson & Johnson
June 29, 2026
On-site
Santa Clara, CA
IT
Purpose
- Drive advanced data strategies and initiatives across Manufacturing and Supply Chain operations. Build data foundations connecting shop-floor systems to cloud data storage to enable real-time insights, advanced analytics, and compliant operations.
Key Responsibilities
- Own data architecture and integration strategy for asset/configuration data across systems and cloud.
- Design, implement, and operate ETL/ELT pipelines with Databricks (Spark, Delta Lake, Workflows), Python/PySpark, and AWS S3 to ingest, normalize, validate, reconcile, and serve manufacturing data (robot logs, telemetry) with SLAs.
- Build batch and streaming pipelines from OT sources (OPC UA, MQTT, MES) into cloud using Python, SQL, Spark/Databricks, and AWS S3 (schema/contract management, partitioning/retention, delivery semantics).
- Create reliable APIs and data services for engineering/IT/business systems.
- Establish data quality, test automation, observability, and alerting (metrics, dashboards, lineage) for audit readiness.
- Ensure compliance with J&J Technology standards and computer systems validation; author validation documentation.
- Support data governance (standards, lineage, quality checks, role-based access).
- Drive change management via documentation, playbooks, training, and adoption tied to KPIs.
Qualifications
- BS/MS in Computer Science, Engineering, Data Science, or related.
- 7+ years (5+ for exceptional candidates) data engineering with hands-on cloud data platforms.
Required Skills
- Databricks (Spark, Delta Lake, Workflows); optimize PySpark jobs.
- AWS S3 and core AWS data services (Kinesis/MSK optional).
- Python, PySpark, SQL; performant ETL/ELT.
- Data governance/quality/lineage and validation/compliance (e.g., GxP, CSV); agile.
- Strong communication; integrate OT data sources (OPC UA, MQTT, MES).
Preferred Skills
- MedTech compliance/data practices; lakehouse patterns; real-time streaming; ML integration.
- Databricks/AWS or related certifications.
- ISA-95 and Unified Naming Space.
- Manufacturing/supply chain domain experience.
Other
- Up to 10% travel; onsite attendance required.
- Drive advanced data strategies and initiatives across Manufacturing and Supply Chain operations. Build data foundations connecting shop-floor systems to cloud data storage to enable real-time insights, advanced analytics, and compliant operations.
Key Responsibilities
- Own data architecture and integration strategy for asset/configuration data across systems and cloud.
- Design, implement, and operate ETL/ELT pipelines with Databricks (Spark, Delta Lake, Workflows), Python/PySpark, and AWS S3 to ingest, normalize, validate, reconcile, and serve manufacturing data (robot logs, telemetry) with SLAs.
- Build batch and streaming pipelines from OT sources (OPC UA, MQTT, MES) into cloud using Python, SQL, Spark/Databricks, and AWS S3 (schema/contract management, partitioning/retention, delivery semantics).
- Create reliable APIs and data services for engineering/IT/business systems.
- Establish data quality, test automation, observability, and alerting (metrics, dashboards, lineage) for audit readiness.
- Ensure compliance with J&J Technology standards and computer systems validation; author validation documentation.
- Support data governance (standards, lineage, quality checks, role-based access).
- Drive change management via documentation, playbooks, training, and adoption tied to KPIs.
Qualifications
- BS/MS in Computer Science, Engineering, Data Science, or related.
- 7+ years (5+ for exceptional candidates) data engineering with hands-on cloud data platforms.
Required Skills
- Databricks (Spark, Delta Lake, Workflows); optimize PySpark jobs.
- AWS S3 and core AWS data services (Kinesis/MSK optional).
- Python, PySpark, SQL; performant ETL/ELT.
- Data governance/quality/lineage and validation/compliance (e.g., GxP, CSV); agile.
- Strong communication; integrate OT data sources (OPC UA, MQTT, MES).
Preferred Skills
- MedTech compliance/data practices; lakehouse patterns; real-time streaming; ML integration.
- Databricks/AWS or related certifications.
- ISA-95 and Unified Naming Space.
- Manufacturing/supply chain domain experience.
Other
- Up to 10% travel; onsite attendance required.