Google Cloud Data Architect IAM Data Modernization

Role

Google Cloud Data Architect – IAM Data Modernization

Dallas, TX / Charlotte, NC / Iselin, NJ / Chandler, AZ / Ohio, Delaware (Hybrid)

Must be a US Citizen/GC only

Identity & Access Management (IAM) Data Modernization – migration of an on‑premises SQL data warehouse to a target‑state Data Lake on Google Cloud (GCP), enabling metrics & reporting, advanced analytics, and GenAI use cases (natural language querying, accelerated summarization, cross‑domain trend analysis) leveraging PySpark‑based processing, cloud‑native Dev Ops CI/CD pipelines, and containerized deployments on Open Shift (OCP) to deliver scalable, secure, and high‑performance data solutions.

• Experience implementing CI/CD pipelines for data and analytics workloads.
• Familiarity with Git‑based source control, build automation, and deployment strategies.
• Experience with Open Shift Container Platform (OCP) for deploying data workloads and services.
• Understanding of containerized architecture, scaling, and environment management.
• Proven ability to build CI/CD pipelines for data and infrastructure workloads.
• Experience managing secrets securely using GCP Secret Manager.
• Ownership of observability, SLOs, dashboards, alerts, and runbooks.
• Proficiency in logging, monitoring, and alerting for data pipelines and platform reliability.
• Hands‑on experience with PySpark for ETL/ELT, data transformation, and performance optimization.
• Solid understanding of distributed data processing concepts.
• Strong experience designing data platforms on Google Cloud Platform (GCP).
• Experience with Data Lakes, data warehousing, and large‑scale migration programs.
• Proven experience designing and implementing data lake architectures (e.g., Bronze/Silver/Gold or layered models).
• Strong knowledge of Cloud Storage (GCS) design, including bucket layout, naming conventions, lifecycle policies, and access controls.
• Experience with Hadoop/HDFS architecture, distributed file systems, and data locality principles.
• Hands‑on experience with columnar data formats (Parquet, Avro, ORC) and compression techniques.
• Expertise in partitioning strategies, backfills, and large‑scale data organization.
• Ability to design data models optimized for analytics and BI consumption.
• Experience building batch and streaming ingestion pipelines using GCP-native services.
• Knowledge of Pub/Sub‑based streaming architectures, event schema design, and versioning.
• Strong understanding of incremental ingestion and CDC patterns, including idempotency and deduplication.
• Hands‑on experience with workflow orchestration tools (Cloud Composer / Airflow).
• Ability to design robust error handling, replay, and backfill mechanisms.
• Experience developing scalable batch and streaming pipelines using Dataflow (Apache Beam) and/or Spark (Dataproc).
• Strong proficiency in Big Query SQL, including query optimization, partitioning, clustering, and cost control.
• Hands‑on experience with Hadoop Map Reduce and ecosystem tools (Hive, Pig, Sqoop).
• Advanced Python programming skills for data engineering, including testing and maintainable code design.
• Experience managing schema evolution while minimizing downstream impact.
• Expertise in Big Query performance optimization and data serving patterns.
• Experience building semantic layers and governed metrics for consistent analytics.
• Familiarity with BI integration, access controls, and dashboard standards.
• Understanding of data exposure patterns via views, APIs, or curated datasets.
• Experience implementing data catalogs, metadata management, and ownership models.
• Understanding of data lineage for auditability and troubleshooting.
• Strong focus on data quality frameworks, including validation, freshness checks, and alerting.
• Experience defining and enforcing data contracts, schemas, and SLAs.

• Hands‑on experience implementing fine‑grained access controls for Big Query and GCS.
• Experience with Sprint planning and helping team technically.
• Strong stakeholder communication and solution‑architecture skills.

• Experience:
  
  10–14+ years in Dev Ops and Data Architecture, 5+ years designing on Pyspark/GCP/OCP at scale; prior on‑prem → cloud migration a must.
• Education:
  
  Bachelor’s/Master’s in Computer Science, Information Systems, or equivalent experience.
• Certifications:
  
  Google Cloud Professional Cloud Architect/Dev Ops/OCP (required or within 3 months). Plus:
  Professional Data Engineer, Security Engineer.
• Flexible work from home options available.