Manager, Data Analysis
About Infinitive
Infinitive is a data and AI consultancy that enables its clients to modernize and operationalize their data to create lasting and substantial value. We bring deep industry and technology expertise to drive and sustain adoption of new capabilities. We match our people and personalities to our clients' culture while bringing the right mix of talent and skills to enable measurable value.
Infinitive has been named Best Small Firms to Work For by Consulting Magazine 8 times, most recently in 2025. Infinitive has also been named a Washington Post Top Workplace, Washington Business Journal Best Places to Work, and Virginia Business Best Places to Work.
We are seeking a Data Analysis Manager to lead the strategic definition, validation, and governance of our clients core business metrics. This is not just a "dashboarding" role; you will be the bridge between raw data engineering and executive decision-making, ensuring that every KPI we track is technically sound, business-relevant, and mathematically accurate.
The ideal candidate possesses a rigorous analytical mindset, deep technical proficiency in Python, SQL, and ETL, and the stakeholder management skills necessary to align diverse business units under a "single source of truth."
Core Responsibilities
1. Metric Strategy & Business Alignment
Validate Value: Partner with domain owners to ensure metrics reflect genuine business value and are outcome-oriented, directly supporting Account Executive (AE) decision-making.
Advisory Partnership: Collaborate with engagement & advisory teams to guide high-level insights and recommendations derived from analytics signals.
Scalability: Establish repeatable patterns and frameworks for metric definition and validation to enable scale and reuse across multiple business domains.
2. Governance & Data Integrity
Authoritative Documentation: Define and document authoritative data sources and calculation logic to ensure enterprise-wide consistency and trust.
Readiness Assessment: Act as the "gatekeeper" for engineering; assess metric readiness across ownership, source integrity, and analytical soundness before moving to the implementation phase.
Lifecycle Management: Oversee the end-to-end metric lifecycle, ensuring every data point is clearly owned, governed, and maintained over time.
3. Risk Mitigation & Technical Oversight
Gap Analysis: Proactively identify inconsistencies, data gaps, and risk areas in definitions that could lead to downstream rework or misinterpretation.
Technical Validation: Use SQL and Python to audit complex datasets, ensuring the underlying ETL processes accurately reflect the intended business logic.