Google Cloud Data Architect & IAM Data Modernization

Vytwo•Prosper, TX

28d•Hybrid

About The Position

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.

Requirements

Must be a US Citizen/ GC only
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
Bachelor’s/Master’s in Computer Science, Information Systems, or equivalent experience.
Google Cloud Professional Cloud Architect/DevOps/OCP (required or within 3 months).
Plus: Professional Data Engineer, Security Engineer

Nice To Haves

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
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
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
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 BigQuery and GCS
Experience with Sprint planning and helping team technically.
Strong stakeholder communication and solution‑architecture skills.

Responsibilities

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/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 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.
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.
Security, Privacy & Compliance: Hands-on experience implementing fine-grained access controls for BigQuery and GCS.
Sprint planning and helping team technically.
Strong stakeholder communication and solution‑architecture skills.