Data Architect with OCP

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 (Google Cloud Platform) , 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 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 / 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 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
• 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 (Google Cloud Platform)
• 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/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 solution‑architecture skills Qualifications

Experience:

[10-14]+ years in DevOps and Data Architecture, 5+ years designing on Pyspark/Google Cloud Platform/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.