We Are

The Work

We are seeking a Data Engineering, Management & Governance Manager to own the technical integrity and delivery of strategic AI insights platforms that translate enterprise data into measurable business value. This role sits at the intersection of data architecture, AI platform engineering, and business value realization. It combines the rigor of a data fabric architect with the commercial instincts of a strategic AI platform builder.

You will design and maintain intelligent data platforms that ingest, model, and govern enterprise data across distributed environments — integrating AI capabilities, connecting to systems like Salesforce and Palantir Foundry, and enabling coverage and conversion metrics that drive sales and operational decisions. You will serve as the technical authority on data quality, ontology design, platform versioning, and integration engineering and ensure the platforms your team builds remain trustworthy, scalable, and impactful over time.

You Are

- Credentials — a proven track record of delivering enterprise data and AI platforms — from strategic architecture through production deployment — in complex client or enterprise environments.
- Capability — hands‑on proficiency across data fabric design, cloud data platforms (Snowflake, Databricks, Azure Synapse, BigQuery), pipeline engineering, and AI‑integrated analytics solutions.
- Commercial Acumen — the ability to connect the tool, the data, and the business — designing platforms that power whitespace identification, KPI measurement, and revenue growth insights.
- Connections — trusted advisor presence with client architects and leaders — able to shape reference architectures, facilitate design sessions,



and communicate technical solutions to non‑technical stakeholders.
- Content — a systems‑builder mindset — able to instrument, version, govern, and maintain platforms so they deliver durable value without accumulating technical debt.

In This Role You Will:

- Architect & Deliver Enterprise Data Fabric Solutions
- Design enterprise data fabric solutions that deliver unified, intelligent, automated access to distributed data across on‑premises and multi‑cloud environments.
- Integrate data virtualization, active metadata, knowledge graphs, cataloging, and automated data integration into a connected data layer that powers analytics and AI.
- Define reference architectures, integration patterns, and governance frameworks that scale across the enterprise and remain maintainable over time.
- Apply data mesh and data product operating models to support self‑service consumption, real‑time data delivery, and decentralized data ownership.
- Build & Maintain Strategic AI Insights Platforms
- Own the technical build, versioning, and ongoing maintenance of strategic AI insights platforms, managing releases, schema evolution, and platform integrity over time.
- Find, ingest, and clean current data sources, expanding platform coverage as new whitespace opportunities, accounts, or market segments are identified.




- Design data models and ontologies that structure whitespace and pipeline data so it is queryable, consistent, and ready to feed downstream AI and analytics workflows.
- Instrument platforms to enable automated coverage and conversion metric extraction, moving from manual survey‑based measurement to reliable, system‑generated KPIs.
- Integrate AI & Enterprise Systems
- Connect AI insights platforms to enterprise systems including Salesforce CRM, Palantir Foundry, and other source systems — enabling automated cohort tagging, pipeline tracking, and KPI measurement to flow end‑to‑end.
- Integrate cloud data platforms (Snowflake, Databricks, Azure Synapse, BigQuery) with AI/ML pipelines and analytics workflows to power intelligent automation and decision support.
- Design and implement API and streaming architectures that support real‑time data delivery and event‑driven AI platform workflows.
- Build and maintain ETL/ELT pipelines and metadata lineage solutions that keep data flowing reliably and traceably across the enterprise data estate.
- Govern Data Quality, Security & Access
- Own data quality governance and establish data validation, monitoring, and remediation practices that keep platform inputs trustworthy over time.
- Handle account and client data responsibly, implementing security, access controls, and data governance practices appropriate for sensitive pipeline and market penetration data.
- Maintain metadata lineage and cataloging so data provenance is auditable and platform outputs are explainable to business and technical stakeholders alike.
- Proactively identify and address data quality risks that could degrade platform reliability or undermine the business value case over time.
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📌 Data Engineering, Management (Redmond)
🏢 Accenture
📍 Redmond