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Python Data Jobs in Michigan (NOW HIRING)

Tech Tammina LLC

Data Engineer

Lansing, MI · On-site

$116.10K - $139.50K/yr

Data Engineer Location: Lansing, MI (Need only locals, F2F is must) Rate: Market Duration ... Python/Scala. 8+ years Oracle. 5+ years' experience with Extract, Transform, and Load (ETL) ...

Pwc

GenAI Python Systems Engineer-Director

Detroit, MI

$155K - $410K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Director & Summary ... is a plus - Proficient in Python and structured/unstructured data - Proficient in SQL and ...

Pwc

GenAI Python Systems Engineer-Manager

Detroit, MI

$99K - $232K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... Python and SQL - Experience with Docker and containerized deployments - Skilled in AI techniques ...

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108.40K - $130.10K/yr

Comprehensive experience with one or more programming languages such as Python, Java, or Rust * Comprehensive experience working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3 ...

Stellantis

Data Engineer

Auburn Hills, MI · On-site

$108.40K - $130.10K/yr

Proficiency in Python for automation, data analysis, or scripting. • Cloud Platforms: Experience with AWS, Azure, or GCP data services (e.g., EMR, Glue, Databricks). • Data Modeling: Familiarity ...

Thecsp

BI Data Engineer

Auburn Hills, MI

$108.40K - $130.10K/yr

Write SQL queries and basic Python scripts to transform and clean data, learning best practices along the way. * Document tables, transformations, and pipelines clearly so the team can rely on your ...

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$11

$51

$75

How much do python data jobs pay per hour?

As of May 30, 2026, the average hourly pay for python data in Michigan is $51.09, according to ZipRecruiter salary data. Most workers in this role earn between $42.12 and $58.03 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Python Data professional, and why are they important?

To thrive as a Python Data professional, you need strong programming skills in Python, a solid understanding of data structures, algorithms, and experience with data analysis or data science, typically supported by a relevant degree. Familiarity with technical tools such as pandas, NumPy, SQL, Jupyter Notebooks, and often cloud platforms or machine learning frameworks is important, and certifications like Microsoft or Google Data certifications can be advantageous. Strong analytical thinking, attention to detail, and effective communication help you extract insights from data and collaborate with stakeholders. These skills and qualities are essential to efficiently process, analyze, and interpret data, driving informed business decisions.

What are some common challenges faced by Python Data professionals when working with large datasets?

Python Data professionals often encounter challenges such as optimizing code to handle large volumes of data efficiently and managing memory usage to prevent slowdowns or crashes. Working with big datasets may require leveraging tools like pandas, NumPy, or Dask, and sometimes integrating with distributed computing systems such as Apache Spark. Additionally, ensuring data quality and managing data pipelines for consistent and accurate results can be demanding. Collaborating closely with data engineers, analysts, and other stakeholders is common to ensure smooth data flow and analysis.

What is a Python Data professional?

A Python Data professional is someone who uses the Python programming language to analyze, process, and interpret data. They work with large datasets, perform data cleaning and transformation, and apply statistical or machine learning techniques to extract insights. These professionals often work in roles such as data analyst, data scientist, or data engineer, and use Python libraries like Pandas, NumPy, and scikit-learn to accomplish their tasks.

What is the difference between Python Data vs Data Analyst?

AspectPython DataData Analyst
Required SkillsPython programming, data manipulation, scriptingExcel, SQL, data visualization
CertificationsPython certifications, data science coursesData analysis certifications, Excel certifications
Work EnvironmentData science teams, programming-heavy rolesBusiness intelligence, reporting teams
Industry UsageTech, finance, healthcareRetail, marketing, finance

Python Data roles focus on programming, data manipulation, and building data pipelines using Python, while Data Analysts primarily analyze data using tools like Excel and SQL to generate reports and insights. Both roles often collaborate but differ in technical depth and tools used.

What are popular job titles related to Python Data jobs in Michigan? For Python Data jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Python Data jobs in Michigan look for? The top searched job categories for Python Data jobs in Michigan are:
Python Data jobs near you

Google Cloud Platform Data Architect

Reliable Software Resources

Detroit, MI • Remote

$58.25 - $75/hr

Other

This job post has expired today. Applications are no longer accepted.

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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