Google Cloud Data Architect IAM Data Modernization

Role : Google Cloud Data Architect IAM Data Modernization

Location : Dallas, TX / Charlotte, NC/ Iselin, NJ, / Chandler, AZ (Hybrid 4 days office)

Highly Preferred OCP exp

Project/Program

Identity & Access Management (IAM) Data Modernization migration of an onpremises SQL data warehouse to a targetstate Data Lake on Google Cloud (Google Cloud Platform), enabling metrics & reporting, advanced analytics, and GenAI use cases (natural language querying, accelerated summarization, crossdomain trend analysis) leveraging PySparkbased processing, cloudnative DevOps CI/CD pipelines, and containerized deployments on OpenShift (OCP) to deliver scalable, secure, and highperformance 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 Google Cloud Platform 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 / CICD

• Experience implementing CI/CD pipelines for data and analytics workloads
• Familiarity with Gitbased 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 Google Cloud Platform 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

• Handson 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 (Google Cloud Platform)
• Experience with Data Lakes, data warehousing, and largescale 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/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

Data Ingestion & Orchestration

Experience building batch and streaming ingestion pipelines using Google Cloud Platform-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

Data Processing & Transformation

Experience developing scalable batch and streaming pipelines using 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 MapReduce 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

Analytics & Data Serving

Expertise in BigQuery 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

Data Governance, Quality & Metadata

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

Good to have

Security, Privacy & Compliance

Hands-on experience implementing fine-grained access controls for BigQuery and GCS

Experience with Sprint planning and helping team technically.

Strong stakeholder communication and solutionarchitecture skills

Qualifications

• Experience: [10 14] years in DevOps and Data Architecture, 5 years designing on Pyspark/Google Cloud Platform/OCP at scale; prior onprem 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.

Salary : $80 - $100