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What is the difference between Remote Data Strategy vs Remote Data Analyst?

AspectRemote Data StrategyRemote Data Analyst
Required CredentialsData-related certifications, such as CDMP or CBIP, and strategic planning skillsDegree in Data Science, Statistics, or related field; proficiency in data analysis tools
Work EnvironmentFocus on developing data strategies, policies, and high-level planning remotelyAnalyze data sets, generate reports, and support decision-making remotely
Employer & Industry UsageUsed in organizations to shape data initiatives and policies across departmentsEmployed in various industries to interpret data and provide insights

Remote Data Strategy professionals focus on creating data policies and strategic plans, while Remote Data Analysts interpret data to support business decisions. Both roles often work remotely and require data-related skills, but their core responsibilities differ significantly.

What are the most commonly searched types of Data Strategy jobs in Michigan? The most popular types of Data Strategy jobs in Michigan are:
What cities in Michigan are hiring for Remote Data Strategy jobs? Cities in Michigan with the most Remote Data Strategy job openings:
Remote Data Strategy jobs near you

Google Cloud Platform Data Architect

Reliable Software Resources

Detroit, MI • Remote

$58.25 - $75/hr

Other

Posted 2 days ago

Job description

Job Role: Google Cloud Platform Data Architect

Location: Detroit, MI

Hire-type: Contract

Experience: 8+ years  |  Detroit, MI (mandatory) — Remote up to 50% travel  

Python

Google Cloud Platform Native

Data Warehousing

BigQuery

Data Modeling

ETL / ELT Pipelines

ABOUT THE ROLE

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.

 

KEY RESPONSIBILITIES

•      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

 

REQUIRED SKILLS

•      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

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