Clean
Pharmaceutical
Director,DataEngineering
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“Director, Data Engineering at Clean. Skills: Data Engineering, AI/ML, Cloud Platforms, Data Governance. Architect end-to-end data pipelines. Own data pipelines”
What You'll Achieve.
Drive measurable value; Ensure data integrity; Ensure scalability; Ensure adherence to standards
Industry & Context.
Problem-solving
Permanent work authorization in the United States
What They're Looking For.
Must Have
Bachelor's degree in Computer Science, 8+ years of experience in data engineering, Demonstrated experience architecting and delivering production-grade data pipelines on cloud data platforms, Knowledge of data modeling, ETL processes, semantic layers, Deep expertise in ELT/ETL frameworks and transformation tooling, Experience in the pharmaceutical, biotech, or life sciences industry, Proven experience with commercial pharmaceutical data, Demonstrated ability to lead and mentor highly technical teams, Excellent problem-solving, communication, and stakeholder management skills, Ability to influence and collaborate with peers, Develop and coach others, Oversee and guide the work of other colleagues
Nice to Have
Kubernetes experience a plus
What You'll Do.
Architect end-to-end data pipelines
Ingest commercial data
Partner with teams to define data products
Deliver purpose-built data products
Optimize for model training
Optimize for batch inference
Optimize for real-time scoring
Lead technical design of data contracts
Codify schema expectations
Codify SLA expectations
Codify ownership expectations
Codify lineage expectations
Develop data infrastructure
Manage data infrastructure
Leverage cloud platforms
Drive adoption of modern engineering patterns
Drive medallion architecture adoption
Drive streaming ingestion adoption
Drive incremental processing adoption
Drive feature store integration adoption
Drive maturity in orchestration platforms
Provide operational observability
Steward semantic layer tooling
Steward data catalog investments
Evaluate processing tradeoffs
Establish data product governance
Enforce data asset standards
Meet privacy regulations
Meet enterprise data policy standards
Build observability infrastructure
Ensure AI consumers trust data
Ensure analytics consumers trust data
Own definition of AI-ready data standards
Operationalize AI-ready data standards
Define feature engineering pipelines
Define vector embedding workflows
Define structured data integration
Define unstructured data integration
Partner with AI/ML engineers
Partner with business translators
Partner with product managers
Define data requirements
Translate business needs
Design ingestion transformations
Communicate development status
Communicate delivery timelines
Support rapid prototyping
Support iterative development
Manage data specialists
Mentor data specialists
Define engineering standards
Define career ladders
Define capability-building roadmaps
Serve as engineering thought leader
Serve as primary escalation point
Bridge business expectations and technical execution
Coordinate resource allocation
Coordinate workload balancing
Coordinate succession planning
How You'll Work.
Team & Collaboration
Cross-functional product pod teams; AI application owners; Pfizer Digital teams; Commercial Analytics teams; AI/ML engineers; Product managers; Compliance stakeholders
Communication Scope
Stakeholder management
Process & Methodology
Roadmap planning
Full Job Description
**ROLE SUMMARY** The AI Acceleration (AIA) function within the Chief Marketing Office (CMO) is the single, business-led engine that owns the design, delivery, and scale-up of priority AI capabilities across Commercial operations. AIA works in tight collaboration with various Pfizer functions to deploy and maintain production-grade AI solutions that simplify how we work and drive measurable value across all processes. The **Director, Data Engineering** will play a pivotal role in enabling AI-driven innovation by overseeing the design, development, and maintenance of robust data infrastructure and pipelines. This position ensures that clean, well-structured, and well-described data flows seamlessly into AI tools, powering advanced analytics and intelligent solutions across the organization. The role will also lead the creation and upkeep of a semantic layer for data sources, ensuring consistency and accessibility for downstream applications. Working closely with Pfizer Digital and Commercial Analytics teams, this leader will collaborate on architecture, data governance, and sourcing strategies while leveraging engineering resources to support production-grade data pipelines. The **Director, Data Engineering** will serve as a key partner to AI/ML engineers, product managers, and compliance stakeholders, ensuring data integrity, scalability, and adherence to regulatory standards. **ROLE RESPONSIBILITIES** **Data Pipeline Development** * Architect and own end-to-end data pipelines from commercial data ingestion (IQVIA, Symphony, DDD, claims, CRM) through raw, conformed, curated, and AI/ML serving layers on cloud lakehouse platforms (Snowflake, Databricks, or equivalent). * Partner with Application, AI/ML and Commercial Analytics teams to define and deliver purpose-built data products optimized for model training, batch inference, and real-time scoring pipelines. * Lead the technical design of data contracts — codifying schema, SLAs, ownership, and lineage expectations be
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