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Accenture

Advises businesses and runs their technology

Data Architect

Data EngineeringConfirmed live in the last 24 hours
Full-timeWashington, DC, USA$103.2k - $196.4kIn-person

About the Job

Requirements

  • Must be a U.S. citizen and able to obtain a Public Trust clearance.
  • Bachelor's degree in Computer Science, Engineering, Data/Information Systems, or a related field.
  • 8–12+ years in data engineering or architecture, including 3–5+ years architecting cloud data platforms, preferably Google Cloud Platform.
  • Experience designing enterprise-grade data lakes, warehouses, and streaming architectures.
  • Hands-on expertise with BigQuery, Google Cloud Storage, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
  • Strong background in identity and access management, customer-managed encryption keys, Virtual Private Cloud Service Controls, encryption, data loss prevention, and governance/lineage tools.
  • Experience with regulatory compliance frameworks including FedRAMP, NIST 800-53, HIPAA, and PCI.
  • Demonstrated leadership in cross-functional technical initiatives.
  • Strong skills with extract, load, and transform/extract, transform, and load, streaming patterns, schema evolution, change data capture, and data modeling techniques.
  • Experience with Terraform, continuous integration/continuous delivery, Airflow/Composer, automated testing, and Git workflows.
  • Proven ability to optimize BigQuery and Google Cloud Storage for performance and cost.
  • Experience with Google Cloud Platform professional certifications such as Cloud Architect or Data Engineer.
  • Federal Government or regulated-industry experience.
  • Kubernetes/OpenShift certifications such as CKA/CKAD or RHCSA/RHCE.
  • Experience with service mesh (Istio), API gateways, multi-cluster strategies, and GitOps at scale.
  • Experience with site reliability engineering practices, incident response, and root cause analysis.
  • Familiarity with Databricks or AWS/Azure equivalents.
  • Strong experience in Vertex AI, feature stores, model registries, and MLOps workflows.
  • Experience with governance and metadata frameworks, stewardship workflows, and data mesh concepts.
  • Experience with third-party tools such as Turbot, CrowdStrike, Nessus, Dynatrace, and Splunk.
  • Advanced BigQuery and Google Cloud Storage performance-tuning expertise.

Responsibilities

  • Define target-state data lake/lakehouse, warehouse, and streaming architectures on Google Cloud Platform.
  • Establish data zones (raw/bronze, curated/silver, semantic/gold) and standards for ingestion, extract/load/transform and extract/transform/load, and consumption.
  • Select and architect the use of BigQuery, Google Cloud Storage, Dataproc, and Dataflow for scalable analytics and artificial intelligence workloads.
  • Develop canonical data models, including third normal form, star schema, Data Vault 2.0, and wide tables.
  • Apply BigQuery optimization techniques, including partitioning, clustering, and materialized views.
  • Implement least-privilege identity and access management, customer-managed encryption keys, Virtual Private Cloud Service Controls, encryption, data loss prevention, and private networking.
  • Establish governance frameworks using Dataplex and Data Catalog with metadata, lineage, and data-quality controls.
  • Ensure compliance with frameworks such as NIST 800-53, FedRAMP, HIPAA, PCI, and SOC 2.
  • Design scalable batch and streaming pipelines using Dataflow, Dataproc, Pub/Sub, and Composer.
  • Integrate event-driven architectures using Cloud Functions and Cloud Run.
  • Partner with Data Science teams to enable Vertex AI pipelines, feature stores, and MLOps.
  • Ensure reproducible machine-learning data pipelines with strong provenance and governance.
  • Optimize BigQuery performance, including slots, reservations, and BI Engine, and optimize Google Cloud Storage storage classes and lifecycle rules.
  • Establish monitoring, logging, key performance indicators, and data-quality standards.
  • Define coding/infrastructure-as-code standards using Terraform, continuous integration/continuous delivery practices, and DevSecOps patterns.
  • Maintain architecture artifacts and conduct architecture reviews.
  • Mentor teams and support governed self-service analytics via semantic layers such as Looker.
  • Maintain architecture blueprints and reference implementations.
  • Define ingestion, schema evolution, and retention standards.
  • Review data models, pipelines, infrastructure as code, and security designs.
  • Conduct proofs of concept, benchmarks, cost analyses, and documentation.

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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