Google Cloud Data Architect & IAM Data Modernization

Vytwo•Prosper, TX

174d•Hybrid

About The Position

The Google Cloud Data Architect will lead IAM data modernization, migrating an on-premises SQL data warehouse to a target-state data lake on Google Cloud. The platform will support metrics and reporting, advanced analytics, and GenAI use cases such as natural-language querying, accelerated summarization, and cross-domain trend analysis. The role uses PySpark-based processing, cloud-native DevOps CI/CD pipelines, and containerized deployments on OpenShift to deliver scalable, secure, high-performance data solutions. The position is hybrid in Dallas, Texas; Charlotte, North Carolina; Iselin, New Jersey; Chandler, Arizona; or Ohio or Delaware. Applicants must be U.S. citizens or permanent residents. Flexible work-from-home options are available. The role covers data and cloud architecture, data lake storage, batch and streaming ingestion, orchestration, data processing and transformation, analytics serving, governance, quality, metadata, security, and platform reliability. The architect will design and implement GCP data platforms and migration programs; develop and optimize pipelines using PySpark and GCP services; establish CI/CD, observability, and operational practices; and support governed analytics and secure data access.

Requirements

10–14+ years of experience in DevOps and Data Architecture.
At least 5 years designing solutions using PySpark, GCP, and OCP at scale.
Prior on-premises-to-cloud migration experience.
Strong experience designing data platforms on Google Cloud Platform.
Hands-on PySpark experience for ETL/ELT, data transformation, and performance optimization, with a solid understanding of distributed data processing.
Experience designing data lake architectures, including layered models such as Bronze/Silver/Gold.
Strong knowledge of 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 Parquet, Avro, or ORC and compression techniques.
Expertise in partitioning strategies, backfills, and large-scale data organization.
Experience building batch and streaming ingestion pipelines with GCP-native services.
Knowledge of Pub/Sub streaming architectures, event schema design, and versioning.
Understanding of incremental ingestion and CDC patterns, including idempotency and deduplication.
Hands-on experience with Cloud Composer/Airflow or similar workflow orchestration tools.
Experience with Dataflow (Apache Beam) and/or Spark (Dataproc) for scalable batch and streaming pipelines.
Strong BigQuery SQL skills, including query optimization, partitioning, clustering, and cost control.
Hands-on experience with Hadoop MapReduce and ecosystem tools such as Hive, Pig, and Sqoop.
Advanced Python programming skills for data engineering, including testing and maintainable code design.
Experience managing schema evolution while minimizing downstream impact.
Experience with BigQuery performance optimization, data serving patterns, semantic layers, governed metrics, BI integration, access controls, and dashboard standards.
Understanding of data exposure through views, APIs, or curated datasets.
Experience implementing data catalogs, metadata management, data ownership, lineage, data quality frameworks, data contracts, schemas, and SLAs.
Bachelor’s or master’s degree in Computer Science, Information Systems, or equivalent experience.
Google Cloud Professional Cloud Architect, Google Cloud DevOps, or OCP certification is required or must be obtained within 3 months.
Must be a U.S. citizen or green card holder.

Nice To Haves

Professional Data Engineer certification.
Professional Security Engineer certification.
Hands-on experience implementing fine-grained access controls for BigQuery and GCS.
Experience with sprint planning and technically supporting a team.
Strong stakeholder communication and solution-architecture skills.

Responsibilities

Design and implement Google Cloud data platforms, data lakes, and large-scale migration solutions, including migration from on-premises SQL data warehouses.
Design layered data lake architectures and GCS storage layouts, including bucket organization, naming conventions, lifecycle policies, access controls, partitioning, and backfills.
Build batch and streaming ingestion pipelines using GCP-native services, including Pub/Sub-based architectures, incremental ingestion, and CDC patterns.
Develop scalable data processing and transformation pipelines using PySpark, Dataflow/Apache Beam, and/or Spark on Dataproc.
Optimize BigQuery SQL, performance, partitioning, clustering, data serving patterns, and cost.
Design data models, semantic layers, governed metrics, and curated data exposure for analytics and BI consumption.
Implement workflow orchestration using Cloud Composer/Airflow and provide robust error handling, replay, and backfill mechanisms.
Implement CI/CD pipelines for data, analytics, and infrastructure workloads using Git-based source control, build automation, and deployment strategies.
Deploy data workloads and services on OpenShift and manage containerized architecture, scaling, and environments.
Securely manage secrets using GCP Secret Manager and implement access controls for data platforms.
Establish and maintain observability, SLOs, dashboards, alerts, runbooks, logging, and monitoring for pipeline and platform reliability.
Implement data catalogs, metadata management, ownership models, lineage, data quality checks, data contracts, schemas, and SLAs.
Support sprint planning and provide technical guidance to the team.
Communicate with stakeholders and contribute solution-architecture expertise.