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Accenture

Advises businesses and runs their technology

Data Engineering, Management & Governance Manager

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of experience in data architecture, enterprise data engineering, or AI platform delivery.
  • At least 5 years of experience architecting data fabric solutions, including data virtualization, active metadata management, knowledge graphs, and automated data integration.
  • At least 5 years of experience with cloud data platforms such as Snowflake, Databricks, Azure Synapse, BigQuery, or equivalent.
  • At least 5 years of experience with data integration, ETL/ELT pipelines, and metadata and lineage tooling.
  • At least 5 years of experience designing API and streaming architectures for real-time data delivery.
  • At least 5 years of experience with enterprise CRM or operational platform integration in data engineering or AI platform delivery; Salesforce is preferred.
  • Demonstrate proficiency with cloud data platforms including Snowflake (including Cortex AI and Snowpark), Databricks, Azure Synapse Analytics, and Google BigQuery.
  • Demonstrate proficiency with data fabric and integration, including data virtualization, active metadata management, knowledge graphs, automated data integration, and data cataloging tools such as Informatica, Alation, or Collibra, or equivalent.
  • Demonstrate proficiency in pipeline engineering using ETL/ELT tools such as dbt, Apache Spark, Azure Data Factory, AWS Glue, or equivalent.
  • Demonstrate proficiency in data modeling and ontology, including dimensional modeling, graph data modeling, ontology design, and schema evolution for AI-ready data platforms.
  • Demonstrate proficiency in streaming and API architecture, including Apache Kafka, Azure Event Hubs, AWS Kinesis, REST/GraphQL API design, and event-driven integration patterns.
  • Demonstrate proficiency in enterprise system integration, including Salesforce, Palantir Foundry, or equivalent enterprise platforms.
  • Demonstrate proficiency in AI/ML platform integration, including connecting data platforms to AI/ML workflows, vector databases, RAG pipelines, and analytical AI models.
  • Demonstrate proficiency in data governance and quality, including metadata lineage, data quality monitoring, access control frameworks, and audit logging for sensitive enterprise data.
  • Demonstrate proficiency in analytics enablement, including platform instrumentation for automated KPI extraction, coverage metrics, and conversion measurement.
  • Demonstrate proficiency in security and access management, including data security best practices, role-based access control, and governance in regulated or sensitive data environments.
  • Demonstrate Python and/or SQL proficiency for pipeline development, data transformation, and platform automation.
  • Applicants for U.S. employment must have work authorization that does not now or in the future require visa sponsorship.

Responsibilities

  • Design and maintain intelligent data platforms that ingest, model, and govern enterprise data across distributed environments, integrating AI capabilities and connecting systems such as Salesforce and Palantir Foundry to enable coverage and conversion metrics for sales and operational decisions.
  • Serve as technical authority on data quality, ontology design, platform versioning, and integration engineering, ensuring platforms remain trustworthy, scalable, and impactful.
  • Design enterprise data fabric solutions providing 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 for analytics and AI.
  • Define reference architectures, integration patterns, and governance frameworks that scale across the enterprise and remain maintainable.
  • Apply data mesh and data product operating models to support self-service consumption, real-time data delivery, and decentralized data ownership.
  • Own the technical build, versioning, and ongoing maintenance of strategic AI insights platforms, managing releases, schema evolution, and platform integrity.
  • Find, ingest, and clean new data sources, expanding platform coverage as whitespace opportunities, accounts, or market segments are identified.
  • Design data models and ontologies that structure whitespace and pipeline data for consistent querying and downstream AI and analytics workflows.
  • Instrument platforms for automated coverage and conversion metric extraction, replacing manual survey-based measurement with system-generated KPIs.
  • Connect AI insights platforms to Salesforce CRM, Palantir Foundry, and other source systems to enable end-to-end automated cohort tagging, pipeline tracking, and KPI measurement.
  • Integrate cloud data platforms with AI/ML pipelines and analytics workflows to power intelligent automation and decision support.
  • Design and implement API and streaming architectures for real-time data delivery and event-driven AI platform workflows.
  • Build and maintain ETL/ELT pipelines and metadata lineage solutions to keep data flowing reliably and traceably across the enterprise data estate.
  • Own data quality governance and establish data validation, monitoring, and remediation practices to keep platform inputs trustworthy.
  • Handle account and client data responsibly by implementing security, access controls, and data governance 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.
  • Identify and address data quality risks that could degrade platform reliability or undermine business value.
  • Engage client data architects and business leaders as a trusted technical advisor, facilitating design sessions, shaping solution architectures, and aligning platform capabilities to business outcomes.
  • Translate requirements such as whitespace identification, account penetration measurement, and revenue growth targeting into platform data models and AI-powered workflows.
  • Partner with pursuit teams and business stakeholders to shape architectures and delivery models for data modernization and AI value programs.
  • Build reusable data assets, pipeline templates, and reference architectures to accelerate delivery across client engagements.
  • Research and evaluate emerging cloud data platforms, data fabric technologies, and AI integration patterns to improve delivered solutions.
  • Apply modern streaming, API, and event-driven architecture patterns to expand platform capabilities and reduce data-delivery latency.
  • Contribute thought leadership on enterprise data fabric, AI-integrated data platforms, and whitespace value engineering to internal communities and client engagements.
  • Travel as required for client and business needs, potentially from 0% to 100%.

Desired Qualifications

  • Salesforce is preferred for experience in enterprise CRM or operational platform integration.
  • Hands-on experience applying data mesh and data product operating models in enterprise delivery contexts.
  • Cloud or data platform architecture certifications in Snowflake, Databricks, AWS, Microsoft Azure, or Google Cloud; data engineering or AI/ML specializations are 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.
  • An MBA or advanced graduate degree in a technical or business discipline.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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