Google Cloud Data Architect – IAM Data Modernization

Role : Google Cloud Data Architect – IAM Data Modernization

Location : Dallas, TX / Charlotte, NC (Hybrid – 4 days office)

Highly Preferred OCP exp

Project/Program

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 DevOps CI/CD pipelines, and containerized deployments on OpenShift (OCP) to deliver scalable, secure, and high‑performance data solutions.

About Program/Project

The IAM Data Modernization project involves migrating an on-premises SQL data warehouse to a target state Data Lake in  GCP cloud environment. Key highlights include:

• Integration Scope: 30+ source system data ingestions and multiple downstream integrations
• Capabilities: Metrics, reporting, and Gen AI use cases with natural language querying, advanced pattern/trend analysis, faster summarizations, and cross-domain metric monitoring
• Benefits:
  • Scalability and access to advanced cloud functionality
  • Highly available and performant semantic layer with historical data support
  • Unified data strategy for executive reporting, analytics, and Gen AI across cyber domains

This modernization establishes a single source of truth for enterprise-wide data-driven decision-making.

Required Skills

DevOps / CI‑CD

• Experience implementing CI/CD pipelines for data and analytics workloads
• Familiarity with Git‑based source control, build automation, and deployment strategies

Containers & Platform

• Experience with OpenShift 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

Big Data & Processing

• Hands‑on experience with PySpark for ETL/ELT, data transformation, and performance optimization
• Solid understanding of distributed data processing concepts

Data & Cloud Architecture

• Strong experience designing data platforms on Google Cloud Platform (GCP)
• Experience with Data Lakes, data warehousing, and large‑scale migration programs

Data Lake Architecture & Storage

• 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/HDFSarchitecture, 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

Data Ingestion & Orchestration

• Experience building batch and streaming ingestion pipelinesusing 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 orchestrationtools (Cloud Composer / Airflow)
• Ability to design robust error handling, replay, and backfill mechanisms

Data Processing & Transformation

• Experience developing scalable batch and streaming pipelinesusing Dataflow (Apache Beam) and/or Spark (Dataproc)
• Strong proficiency in BigQuery SQL, including query optimization, partitioning, clustering, and cost control.
• Hands-on experience with Hadoop MapReduceand ecosystem tools (Hive, Pig, Sqoop)
• Advanced Python programming skillsfor data engineering, including testing and maintainable code design
• Experience managing schema evolutionwhile minimizing downstream impact

Analytics & Data Serving

• Expertise in BigQuery performance optimizationand data serving patterns
• Experience building semantic layers and governed metricsfor consistent analytics
• Familiarity with BI integration, access controls, and dashboard standards
• Understanding of data exposure patterns via views, APIs, or curated datasets

Data Governance, Quality & Metadata

• Experience implementing data catalogs, metadata management, and ownership models
• Understanding of data lineagefor auditability and troubleshooting
• Strong focus on data quality frameworks, including validation, freshness checks, and alerting
• Experience defining and enforcing data contracts, schemas, and SLAs

 Good to have

Security, Privacy & Compliance

• Hands-on experience implementing fine-grained access controlsfor BigQuery and GCS
• Experience with Sprint planning and helping team technically.
• Strong stakeholder communication and solution‑architecture skills

Qualifications

• Experience: [10–14]+ years in DevOps 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/DevOps/OCP (required or within 3 months). Plus: Professional Data Engineer, Security Engineer.

 Other Job Details:

• Job Type: C2C or W2
• Pay Rate: $60-65 hr on C2C / $55/hr on W2
• Duration: 12 months (high possibility of extension)
• Location:  Dallas, TX / Charlotte, NC (Hybrid – 4 days in office)
• Docs Required: ID proof will be required

Please review the job description and let me know if it aligns with your experience. Looking forward to your response.