Data & Analytics Practice Leader

Role Story

Our client currently has approximately 25 data stewards manually processing inbound client data files daily. Much of the data arrives inconsistent, poorly formatted, and difficult to ingest, creating operational inefficiencies and limiting scalability.

The goal of this role is to modernize the data platform, define canonical business definitions, and automate ~80% of manual data ingestion processes through improved architecture, standardized data models, and modern cloud tooling.

In Q2 and Q3 2026, a private equity sponsor will build the initial data platform and implement the foundational medallion architecture. This leader will take ownership of that platform, extend it, and build the team and processes around it to drive long-term value.

This individual will:

• Absorb the Service Bureau team (approximately 25 data stewards) under Product & Engineering and assess team capabilities
• Take ownership of the PE-built data platform and medallion architecture, extending and maturing it
• Define canonical business definitions and a KPI dictionary for enterprise metrics
• Build operational read models replacing legacy stored-procedure-driven reporting
• Reduce manual service bureau dependency through automation and self-service tooling
• Hire a Data Engineering Manager and build out the data engineering team for long-term platform ownership
• Support board KPI dashboard by defining metrics and ensuring data access and quality

This role requires both deep data architecture capability and strong leadership ability.

Overview

Our client is seeking a Data & Analytics Practice Leader to modernize the company’s data architecture and lead transformation from manual data processing to a scalable, cloud-based data platform.

This role will lead a team currently responsible for ingesting, cleansing, and mapping inbound customer data files, while owning the long-term data architecture supporting automation, analytics, and machine learning initiatives. The role reports to the CPTO.

Key Responsibilities

Data Architecture & Canonical Definitions

• Take ownership of PE-built data platform and medallion architecture (bronze, silver, gold layers)
• Define canonical business definitions for core entities (participant, employer, plan, claim, contribution, enrollment) and build an enterprise KPI dictionary
• Build operational read models (Azure SQL) replacing stored-procedure-driven reporting, eliminating direct legacy database access for reporting
• Define data standards improving quality and interoperability across systems
• Partner with Principal Architect to align data platform with enterprise architecture strategy

Platform Ownership & Extension

• Own and extend the data platform after PE handoff, including pipeline development and data quality rules
• Define scalable data ingestion and transformation pipelines
• Plan and execute migration from VisualCron to Boomi for data pipeline orchestration
• Create data architecture supporting future ML and AI use cases, including potential migration to Databricks

Team Leadership

• Lead and assess team of approximately 25 data stewards transitioning into Product & Engineering
• Hire a Data Engineering Manager as a direct report, then build out the data engineering team for platform ownership
• Identify opportunities to automate manual processes and build self-service capabilities
• Improve turnaround time for customer data processing workflows

Operational Transformation

• Define and execute roadmap reducing reliance on manual service bureau model (target: 80% manual to 50% within first year)
• Automate top 5 repetitive data ingestion tasks as immediate quick wins
• Improve data quality through API-based and structured ingestion patterns
• Establish governance around data definitions and structure
• Define and deliver KPI metrics supporting the board dashboard
• Partner with product and engineering leadership to align data capabilities with platform roadmap

First 90-Day Expectations

30 Days

• Understand existing data structures and team capabilities
• Absorb Service Bureau team
• Assess current ingestion workflows and pain points
• Receive PE platform handoff briefing

60 Days

• Define target data architecture and modernization roadmap
• Identify top 5 automation opportunities
• Draft canonical entity definitions for first 5 business objects
• Define KPI dictionary v1

90 Days

• Begin implementing automation quick wins
• Deliver first operational read model
• Align roadmap with Principal Architect and Engineering leadership
• Define hiring plan for Data Engineering Manager and team

Ideal Background

• Strong data architecture experience including medallion architecture and modern data platforms
• Experience transforming manual data operations into automated, scalable pipelines
• Experience with Azure Synapse, Microsoft Fabric, Databricks, or Snowflake
• Experience defining canonical data models and enterprise business definitions
• Experience building and leading data engineering teams
• Experience working with messy, inconsistent data environments at scale
• Strong leadership capability with hands-on technical depth
• Experience working in regulated environments preferred