What are the responsibilities and job description for the Associate Director, Data Engineering - (Contract) position at Bounteous?
Role Summary
The Associate Director, Data Engineering owns the data architecture, quality, and integration backbone connecting the platform to the client's system-of-record landscape (MDM, ERP, and third-party enrichment sources) and to downstream reporting/analytics. This role leads a data engineering team responsible for resolving persistent data-quality and system-sync defects and building the reporting layer that supports SLA, cycle-time, and forecast-accuracy metrics. This engagement uses an agent-orchestrated development model, so hands-on AI-assisted development experience is required here as well.
Note: This role is 20 hours per week work.
Key Responsibilities
The Associate Director, Data Engineering owns the data architecture, quality, and integration backbone connecting the platform to the client's system-of-record landscape (MDM, ERP, and third-party enrichment sources) and to downstream reporting/analytics. This role leads a data engineering team responsible for resolving persistent data-quality and system-sync defects and building the reporting layer that supports SLA, cycle-time, and forecast-accuracy metrics. This engagement uses an agent-orchestrated development model, so hands-on AI-assisted development experience is required here as well.
Note: This role is 20 hours per week work.
Key Responsibilities
- Own the data architecture strategy connecting the CRM/onboarding platform, MDM, and ERP systems, ensuring a single, trusted flow of customer and account data across systems.
- Lead resolution of data-integrity defects: field mapping mismatches, cross-system sync gaps, and data carryover issues between intake and downstream systems.
- Design and govern the data validation/enrichment pipeline integrating third-party sources, prioritizing API-based integration over manual/portal workflows where feasible.
- Own the data model and pipeline design supporting AI-driven document data extraction and validation feedback loops.
- Build and lead the reporting/analytics data layer covering cycle time, SLA adherence, bottleneck identification, queue prioritization impact, data completeness/validation error rates, and forecast accuracy vs. actual go-live dates.
- Lead data governance for sensitive data flows (e.g., PHI) in coordination with the client's compliance and security teams.
- Direct and leverage AI coding agents (e.g., Claude Code or equivalent) for pipeline development, data validation logic, and rapid prototyping of integration fixes.
- Manage and grow a team of data engineers/analysts; set technical standards for data pipeline development, testing, and monitoring.
- Partner with the Principal Architect and Solution Architect to ensure data model decisions are compatible with downstream MDM, ERP, and reporting requirements.
- Present data quality, integration health, and reporting-accuracy findings to program leadership and client stakeholders as needed.
- 12 years in data engineering/architecture roles with 5 years leading data engineering teams in an enterprise transformation context.
- Proven experience designing and troubleshooting data integrations between CRM, ERP, and master data management platforms.
- Strong background in data quality remediation — field mapping, sync architecture, and batch/real-time integration defect resolution at enterprise scale.
- Experience building reporting/analytics pipelines supporting operational metrics for business stakeholders.
- Hands-on experience directing or prompting AI coding agents (e.g., Claude Code or an equivalent agentic development tool) as part of an agent-orchestrated delivery model — required.
- People-management experience leading a data engineering team through concurrent, high-stakes delivery streams.
- Familiarity with PHI/regulated data handling requirements and working alongside compliance/security teams.
- Experience with enterprise integration/middleware platforms.
- Background in pharmaceutical distribution, healthcare, or GPO/rebate data models.
- Familiarity with AI/LLM-based data extraction pipelines.
- Relevant data architecture or platform-specific data management certifications.