Data Engineering, Management & Governance Manager

Accenture•St. Louis, MO

1d•$80,400 - $293,800•Remote

About The Position

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.

Requirements

Minimum of 5 years of experience in the following:
Data architecture, enterprise data engineering, or AI platform delivery.
Architecting data fabric solutions — including data virtualization, active metadata management, knowledge graphs, and automated data integration.
With cloud data platforms: Snowflake, Databricks, Azure Synapse, BigQuery, or equivalent.
With data integration, ETL/ELT pipelines, and metadata and lineage tooling.
Designing API and streaming architectures for real-time data delivery.
With enterprise CRM or operational platform integration (Salesforce preferred) in the context of data engineering or AI platform delivery
Cloud Data Platforms: Snowflake (including Cortex AI, Snowpark), Databricks, Azure Synapse Analytics, Google BigQuery.
Data Fabric & Integration: Data virtualization, active metadata management, knowledge graphs, automated data integration, and data cataloging (e.g., Informatica, Alation, Collibra, or equivalent).
Pipeline Engineering: ETL/ELT design and delivery using tools such as dbt, Apache Spark, Azure Data Factory, AWS Glue, or equivalent.
Data Modeling & Ontology: Dimensional modeling, graph data modeling, ontology design, and schema evolution for AI-ready data platforms.
Streaming & API Architecture: Apache Kafka, Azure Event Hubs, AWS Kinesis, REST/GraphQL API design, and event-driven integration patterns.
Enterprise System Integration: Salesforce (data integration and pipeline tagging), Palantir Foundry, or equivalent enterprise platforms.
AI/ML Platform Integration: Connecting data platforms to AI/ML workflows, vector databases, RAG pipelines, and analytical AI models.
Data Governance & Quality: Metadata lineage, data quality monitoring, access control frameworks, and audit logging for sensitive enterprise data.
Analytics Enablement: Instrumentation of platforms for automated KPI extraction, coverage metrics, and conversion measurement.
Security & Access Management: Data security best practices, role-based access control, and governance in regulated or sensitive data environments.
Programming: Python and/or SQL proficiency for pipeline development, data transformation, and platform automation.

Nice To Haves

Hands-on experience applying data mesh and data product operating models in enterprise delivery contexts.
Cloud or data platform architecture certifications: Snowflake, Databricks, AWS, Microsoft Azure, or Google Cloud (data engineering or AI/ML specializations preferred).
Direct experience in Oil & Gas, energy, utilities, or industrial sectors — particularly with OT data environments, time-series data, or sector-specific AI use cases.
Experience with Palantir Foundry data modeling, ontology design, or pipeline development.
Familiarity with SAFe (Scaled Agile Framework) or enterprise delivery frameworks at scale.
Experience contributing to open-source data platform or AI engineering projects, or published thought leadership in enterprise data architecture.
MBA or advanced graduate degree in a technical or business discipline.