Google Cloud Data Architect – IAM Data Modernization

    H1B Workable  Fine with Relocation      Role : Google Cloud Data Architect – IAM Data Modernization Location : Dallas, TX / Carlotte, NC/ Columbus Ohio / New jersey (4 days onsite) Implementation partner:************************** End Client  - (Domain ) Banking  / Finance Mode of Interview - Video / Virtual Experience: 12+ years     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 (Google Cloud Platform), enabling metrics & reporting, advanced analytics, and GenAI use cases (natural language querying, accelerated summarization, cross‑domain trend analysis).   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 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   Qualifications Experience: [10–14]+ years in data engineering/architecture, 5+ years designing on Google Cloud Platform 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 (required or within 3 months). Plus: Professional Data Engineer, Security Engineer.     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 ·       Familiarity with audit logging and compliance readiness   Cloud Platform Management ·       Strong hands-on experience with Google Cloud Platform (Google Cloud Platform), including project setup, environment separation, billing, quotas, and cost controls ·       Expertise in IAM and security best practices, including least-privilege access, service accounts, and role-based access ·       Solid understanding of VPC networking, private access patterns, and secure service connectivity ·       Experience with encryption and key management (KMS, CMEK) and security auditing   DevOps, Platform & Reliability ·       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 Good to have Security, Privacy & Compliance ·       Hands-on experience implementing fine-grained access controls for BigQuery and GCS ·       Experience with VPC Service Controls and data exfiltration prevention ·       Knowledge of PII handling, data masking, tokenization, and audit requirements