Staff Data Engineer — AI Data Platform Leader

Becton, Dickinson and Company (BD)

Irvine (CA)

On-site

USD 161,000 - 258,000

Full time

9 days ago

Application generator

A complete application in a minute — tailored resume and cover letter, ready to send.

Get past ATS filters

Job summary

BD is seeking a Staff Data Engineer to lead the design and evolution of the data platform powering BD Connected Care's AI initiatives. You will architect scalable pipelines and governance solutions, delivering trusted data to ML engineers and other consumers across the organization.

The role combines software development, data architecture, and platform strategy, requiring collaboration with engineering, product, and clinical teams to onboard diverse sources while maintaining data quality and

Qualifications

Bachelor's in Computer Science, Data Engineering, or related field.
7+ years of data engineering experience with end-to-end ownership of pipelines and data models.
Architectural ownership with standards and decisions influencing other engineers.
Expert SQL: query authoring, optimization, partitioning, distribution keys, window functions.
Production experience with AWS data services and data lakehouse tools (Redshift, S3, Glue, Lambda).
Experience with data modeling, governance, lineage, and data quality practices.
Hands-on feature engineering for ML pipelines with correct timing and leakage avoidance.
Ability to translate operational and clinical processes into data inputs for modeling.
Ownership of data quality with validation and anomaly detection in production.
HIPAA/PHI handling, access control, masking, de-identification in production environments.
Operational maturity: SLAs, monitoring, on-call, incident response.
Experience in Agile/Scrum and Confluence documentation.

Responsibilities

Own data architecture for BD AI workflows, including warehouse, lake, and serving layers.
Design and implement ingestion from BD sources into analytics and ML data layers.
Model relational and analytical schemas for forecasting and feature stores.
Build reconciled histories across systems with accurate timestamps.
Own data quality contracts and automated validation across feeds.
Define SLAs, monitoring, incident response, and post-incident reviews.
Ensure HIPAA compliance, lineage, and access controls across pipelines.
Mentor engineers and collaborate with ML teams on features and datasets.