Associate Director, Data Enablement & Operations - Patient Services & Value & Access

Main Responsibilities & Technical Competencies

Data Engineering & Pipeline Development

• Design, build, test, deploy, and maintain scalable batch and near-real-time pipelines using Snowflake, Databricks, SQL, Python, and approved orchestration tools.
• Develop ingestion and transformation workflows for Hub, specialty pharmacy, specialty distributor, payer, CRM, claims, and access-related data.
• Create reusable frameworks, source-to-target mappings, data models, and automated validation controls.
• Optimize pipelines, queries, storage, and compute for performance, reliability, maintainability, and cost.
• Perform hands-on root-cause analysis and code remediation for failed jobs, defects, reconciliation issues, and downstream impacts.

Patient Services & Value & Access Data Enablement

• Serve as the primary data enablement and aggregation point of contact for Patient Services and Value & Access.
• Translate business workflows and reporting needs into analytics-ready data products and technical specifications.
• Enable data supporting patient enrollment, access progression, benefits verification, prior authorization, affordability, fulfillment, and adherence.
• Aggregate and harmonize data across brands, programs, vendors, and channels with appropriate controls for sensitive data.
• Define data acceptance criteria, refresh expectations, reconciliation rules, and service levels with business owners.

Salesforce Life Sciences Cloud & Integration

• Understand Salesforce Life Sciences Cloud data structures, workflows, integration events, and downstream requirements.
• Design and support interfaces among Salesforce Life Sciences Cloud, Hub and specialty pharmacy partners, Snowflake, Databricks, and reporting platforms.
• Validate completeness, accuracy, timeliness, and traceability of inbound and outbound Salesforce feeds.
• Assess downstream impact of CRM releases and coordinate required pipeline or model changes.

Analytics Enablement & Data Products

• Partner with Data Strategy & Analytics to provide certified datasets, governed metrics, semantic-ready data, and documented business logic.
• Convert recurring analytical requirements into durable data models rather than one-time extracts.
• Support controlled SQL/Python analysis while industrializing repeatable needs.
• Enable traceability from source through transformations to reports, metrics, and analytical outputs.
• Collaborate with analysts and data scientists on feature-ready datasets and productionization of approved logic.

Data Quality, Governance & Compliance

• Implement automated controls for completeness, accuracy, validity, timeliness, duplication, referential integrity, and reconciliation.
• Own monitoring, issue triage, incident resolution, runbooks, lineage, technical documentation, and audit evidence.
• Apply role-based access, least privilege, privacy, retention, and approved-use requirements.
• Partner with Privacy, Legal, Compliance, Security, and platform teams in a regulated environment.
• Communicate data risks, root cause, remediation, and recurrence-prevention actions.

Operating Model, Delivery & Vendor Management

• Manage intake and prioritization of requests, enhancements, defects, and new-source onboarding.
• Coordinate internal teams and external vendors with clear requirements, dependencies, acceptance criteria, and service expectations.
• Monitor feed health, delivery timeliness, quality trends, incidents, and backlog status.
• Maintain code in approved repositories and enforce testing, release, version-control, and documentation standards.
• Build reusable capabilities that reduce manual effort, vendor dependency, and onboarding time.

Success Measures

• Reliable and timely delivery of trusted Patient Services and Value & Access data products.
• Reduced manual aggregation and one-time pulls through reusable pipelines and curated datasets.
• Improved data quality, reconciliation coverage, lineage, documentation, and issue-resolution time.
• Faster onboarding of vendors, programs, brands, feeds, and analytical use cases.
• Adoption of certified datasets and governed metrics by analytics and reporting teams.