> Markdown version of [/jobs/ext/3780751-lead-google-cloud-platform-solution-architect-role](https://www.wearedevelopers.com/jobs/ext/3780751-lead-google-cloud-platform-solution-architect-role). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Lead Google Cloud Platform Solution Architect Role

- **Company:** DATAFACTZ LLC
- **Location:** Detroit, MI, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Airflow, BigTable, BigQuery, Cloud Computing, Cloud Database, Cloud Storage, Data Architecture, Data Transmissions, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Systems, Data Vault Modeling, Data Warehousing, Data Flow Control, Python (Programming Language), Query Optimization, Cloudera, SQL Databases, Data Streaming, User-Centered Design, Parquet, Google Cloud, Data Ingestion, Snowflake, Pyspark, Semi-structured Data, Data Lineage, Collibra, Avro, Star Schema, Physical Data Models, Stream Analytics, Data Pipelines, Api Management, Apache Beam
- **Published:** October 16, 2026
- **Apply:** https://www.dice.com/job-detail/0ec8d430-47ae-403b-9889-4e84799803fc

## About the Role

* Python: Advanced proficiency for pipeline development, data transformation scripts, and Google Cloud Platform SDK/API integrations

 * Google Cloud Platform expertise: Deep hands-on experience with BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud SQL

 * Data warehousing: Proven experience designing enterprise-scale data warehouses with dimensional and vault modeling techniques

 * Data modeling: Strong ability to design logical and physical models for analytical and operational workloads on BigQuery

 * ETL/ELT pipelines: Designing and overseeing large-scale batch and streaming data pipelines for structured and semi-structured data

 * SQL: Expert-level BigQuery SQL including window functions, nested/repeated fields, partitioning, and query optimization

 * Leadership: Ability to lead architecture decisions, align cross-functional teams, and mentor engineers

PREFERRED

 * Google Cloud Platform certifications: Professional Data Engineer or Professional Cloud Architect

 * Experience with dbt Cloud for BigQuery transformation and documentation

 * Familiarity with data catalog tools: Dataplex, Data Catalog, or Collibra on Google Cloud Platform

 * Exposure to real-time analytics patterns using BigQuery streaming inserts or Bigtable

## Description

As a Google Cloud Platform Data Architect at DataFactZ you will own the end-to-end design of cloud-native data warehouse and data platform solutions on Google Cloud. You will define data architecture standards, establish data modeling patterns, and lead the design of scalable ingestion and transformation pipelines - working hands-on with engineering teams to deliver production-grade data systems for enterprise clients., * Architect enterprise data warehousing solutions on Google Cloud Platform using BigQuery as the primary analytical platform, including logical and physical data model design

 * Design and implement data modeling patterns: star schema, snowflake, data vault, and wide-table approaches optimized for BigQuery performance and cost

 * Define lakehouse architectures across BigQuery and Cloud Storage using Parquet, Avro, and ORC formats with appropriate partitioning and clustering strategies

 * Lead the design of batch and streaming ingestion pipelines using Dataflow (Apache Beam), Dataproc (PySpark), Pub/Sub, and BigQuery Data Transfer Service

 * Establish transformation layer standards using dbt or Python-based ELT patterns within BigQuery

 * Design pipeline orchestration frameworks using Cloud Composer (Airflow) for complex multi-step workflows

 * Define data governance standards: schema management, data lineage, access controls, and partitioning policies across Google Cloud Platform projects

 * Lead technical discovery with client stakeholders, produce architecture decision records, and translate business requirements into data platform designs

 * Mentor data engineers and ensure adherence to architecture standards across delivery teams