Posted 5 days ago
Data Architect
AI Summary
Designs and maintains enterprise data architectures on GCP, including data lakes, warehouses, and streaming systems, while ensuring security, compliance, and performance optimization.
About this role
Key Responsibilities
Enterprise Architecture (GCP)
- Define target‑state data lake/lakehouse, warehouse, and streaming architectures on GCP.
- Establish data zones (raw/bronze, curated/silver, semantic/gold) and standards for ingestion, ELT/ETL, and consumption.
- Select and architect use of BigQuery, GCS, Dataproc, and Dataflow for scalable analytics and AI workloads.
Data Modeling & Design
- Develop canonical data models (3NF, star schema, Data Vault 2.0, wide tables).
- Apply BigQuery optimization techniques including partitioning, clustering, and materialized views.
Security, Compliance & Governance
- Implement least‑privilege IAM, CMEK, VPC Service Controls, encryption, DLP, 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.
Pipeline Architecture & Integration
- Design scalable batch and streaming pipelines leveraging Dataflow, Dataproc, Pub/Sub, and Composer.
- Integrate event‑driven architectures using Cloud Functions/Run.
AI/ML Enablement
- Partner with Data Science teams to enable Vertex AI pipelines, feature stores, and MLOps.
- Ensure reproducible ML data pipelines with strong provenance and governance.
Performance, Observability & Cost Optimization
- Optimize BigQuery performance (slots, reservations, BI Engine) and GCS storage classes/lifecycle rules.
- Establish monitoring, logging, KPIs, and data quality standards.
Standards, Documentation & Leadership
- Define coding/IaC (Terraform) standards, CI/CD practices, and DevSecOps patterns.
- Maintain architecture artifacts and conduct architecture reviews.
- Mentor teams and support governed self‑service analytics via semantic layers (Looker).
Day‑to‑Day Duties
- Maintain architecture blueprints and reference implementations.
- Define ingestion, schema evolution, and retention standards.
- Review models, pipelines, IaC, and security designs.
- Conduct POCs, benchmarks, cost analyses, and documentation.
Required Qualifications
- Must be a U.S. Citizen with ability to obtain a Public Trust clearance.
- Bachelor’s degree in Computer Science, Engineering, Data/Information Systems, or related field.
- 8–12+ years in data engineering/architecture, with 3–5+ years architecting cloud data platforms (preferably GCP).
- Experience designing enterprise‑grade data lakes, warehouses, and streaming architectures.
- Hands‑on expertise with BigQuery, GCS, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
- Strong background in IAM, CMEK, VPC SC, encryption, DLP, and governance/lineage tools.
- Experience with regulatory compliance (FedRAMP, NIST 800‑53, HIPAA, PCI, etc.).
- Demonstrated leadership in cross‑functional technical initiatives.
- Strong skills with ELT/ETL, streaming patterns, schema evolution, CDC, and data modeling techniques.
- Experience with Terraform, CI/CD, Airflow/Composer, automated testing, and Git workflows.
- Proven ability to optimize BigQuery and GCS for performance and cost.
- Excellent communication and stakeholder engagement skills.
Preferred Experience
- GCP professional certifications (Cloud Architect, Data Engineer).
- Federal Government or regulated‑industry experience.
- Kubernetes/OpenShift certifications (CKA/CKAD, RHCSA/RHCE).
- Experience with service mesh (Istio), API gateways, multi‑cluster strategies, and GitOps at scale.
- Experience with SRE practices, incident response, and RCA.
- 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 (Turbot, CrowdStrike, Nessus, Dynatrace, Splunk).
Advanced BigQuery and GCS performance‑tuning expertise.
As required by local law, Accenture Federal Services provides reasonable ranges of compensation for hired roles based on labor costs in the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia. The base pay range for this position in these locations is shown below. Compensation for roles at Accenture Federal Services varies depending on a wide array of factors, including but not limited to office location, role, skill set, and level of experience. Accenture Federal Services offers a wide variety of benefits. You can find more information on benefits here. We accept applications on an on-going basis and there is no fixed deadline to apply.
Skills
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