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Data Engineer Project Jobs in Southfield, MI (NOW HIRING)

Mariana Minerals

Sr. Data Engineer

Ann Arbor, MI · On-site

$140K - $200K/yr

Why This Role We own the projects, generate the data, and close the loop. Every facility we build ... Engineer Out Requirements, then Automate - We simplify, optimize, and then automate for scale.

Pwc

Data Strategy-Manager

Detroit, MI

$99K - $232K/yr

Take ownership of projects, ensuring their successful planning, budgeting, execution, and ... Data Engineer / Azure Solutions Architect - Google Professional Data Engineer - DAMA CDMP ...

CONTROLTEC LLC

Project Engineer

Allen Park, MI · On-site

The CT solution combines comprehensive data acquisition methods with a powerful edge and cloud ... This position will consist of developing and supporting various engineering projects related to the ...

CONTROLTEC LLC

Project Engineer

Allen Park, MI · On-site

The CT ® solution combines comprehensive data acquisition methods with a powerful edge and cloud ... This position will consist of developing and supporting various engineering projects related to the ...

Venteon

Civil Project Manager

Detroit, MI · On-site

$115K/yr

Develop bid proposals, schedules, and value-engineering options. * Participate in pre-bid meetings and site visits to assess project conditions. * Maintain historical cost data and update estimating ...

Horstman Inc

Mechanical Engineer

Sterling Heights, MI · On-site

... project updates, technical data, and test outcomes to customers and internal teams, communicating milestones, risks, and next steps. • Collaborate with senior engineering staff to develop new ...

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Showing results 21-40

All JobsData Engineer Project JobsData Engineer Project Jobs in Southfield, MI

Data Engineer Project information

See Southfield, MI salary details

$42.2K

$151.4K

$223.4K

How much do data engineer project jobs pay per year?

As of Aug 23, 2026, the average yearly pay for data engineer project in Southfield, MI is $151,381.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $156,000.00 per year, depending on experience, location, and employer.

What is a data engineer project?

A Data Engineer Project refers to a specific initiative or assignment undertaken by data engineers to design, build, and maintain systems that gather, process, and store large volumes of data. These projects often involve creating data pipelines, integrating multiple data sources, ensuring data quality, and optimizing storage solutions for analytics or business intelligence. Such projects are critical for organizations to manage their data efficiently and enable data-driven decision-making. Data Engineer Projects can range from building a data warehouse to implementing real-time data streaming solutions.

What are some common challenges faced by data engineers working on project-based teams?

Data Engineers on project-based teams often encounter challenges such as integrating data from disparate sources, ensuring data quality and consistency, and meeting tight project deadlines. Collaboration with data scientists, analysts, and software engineers is crucial, requiring clear communication to translate business needs into robust data pipelines. Additionally, adapting to evolving technologies and toolsets is essential for the successful delivery of scalable and maintainable solutions.

What are the key skills and qualifications needed to thrive as a data engineer, and why are they important?

To thrive as a Data Engineer, you need strong proficiency in programming (Python, Java, or Scala), data modeling, and database management, often supported by a degree in computer science or a related field. Familiarity with big data tools (like Hadoop, Spark), ETL systems, cloud platforms (AWS, Azure, GCP), and relevant certifications is highly beneficial. Analytical thinking, problem-solving, and effective communication are crucial soft skills for collaborating with data teams and stakeholders. These competencies are essential for building reliable data pipelines and ensuring data availability and quality to drive business insights.

What is the difference between Data Engineer Project vs Data Engineer?

AspectData Engineer ProjectData Engineer
CredentialsTypically requires a degree in Computer Science, Data Science, or related fields; certifications like AWS, Google Cloud, or Azure are commonSimilar credentials; often holds certifications in cloud platforms and data tools
Work EnvironmentProject-based, often temporary teams working on specific data solutionsFull-time role within organizations, maintaining ongoing data pipelines and infrastructure
Industry UsageUsed across industries for specific data initiativesCore role in data-driven companies and departments
Search & Comparison IntentOften searched for project-based roles or freelance opportunitiesMore common in job searches for permanent positions

In summary, Data Engineer Projects focus on temporary, goal-specific data tasks, while Data Engineers hold ongoing roles responsible for maintaining data infrastructure. Both roles require similar skills and certifications but differ mainly in scope and employment type.

Are data engineers still in high demand?

Data engineers are currently in high demand due to the increasing reliance on data-driven decision making and the growth of big data technologies. Skills in cloud platforms, data pipeline development, and tools like SQL, Python, and Apache Spark enhance job prospects in this field.

Is a data engineer paid well?

Data engineers are generally well-compensated due to their specialized skills in managing large datasets, working with tools like SQL, Python, and cloud platforms. Salaries vary by experience, location, and industry, but they tend to be higher than average for tech roles, reflecting the demand for data infrastructure expertise.

What job categories do people searching Data Engineer Project jobs in Southfield, MI look for?

The top searched job categories for Data Engineer Project jobs in Southfield, MI are:

Data Engineer Project jobs near you
Infographic showing various Data Engineer Project job openings in Southfield, MI as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $151,381 per year, or $72.8 per hour.

Sr. Data Engineer

Mariana Minerals

Ann Arbor, MI • On-site

$140K - $200K/yr

Full-time

Re-posted 14 days ago

Job description

About Mariana Minerals
Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. We're reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making.
The Role
Mariana Minerals is building the critical minerals supply chain from the ground up-and we're looking for a Senior Data Engineer to help make it autonomous.
We're not a software company selling tools to mining operators. We are a mining company that builds software. Mariana designs, builds, commissions, and operates our own mines and refineries. We develop proprietary chemical processes and run them at lab, pilot, and commercial scale. Today, we're producing battery-grade lithium salts from real oil and gas wastewater in our facilities. Our first commercial-scale lithium production facility, Lithium One, is targeting initial production in Q1 of 2027.
As a Senior Data Engineer at Mariana, you'll own a data domain end-to-end-designing the pipelines, schemas, and contracts that make a whole class of plant data trustworthy and queryable. The systems you build are the foundation every model and every operational decision depends on.
The Tech
This is some of the most interesting applied data work happening today.
Our internal platform, PlantOS, uses the same reinforcement learning toolkits that power self-driving vehicles and humanoid robots-but applied to autonomous, short-interval control of mineral refining circuits. None of it works without data: every set point those models adjust, and every decision we make about a plant, rests on turning messy industrial reality into trustworthy, queryable, model-ready data.
The environment is noisy and non-stationary: sensors drift, lab results arrive late and malformed, wastewater compositions shift, equipment ages. The data backbone has to keep up. The end goal is fully autonomous refining operations-and the pipelines you build are the foundation everything else stands on.
What You'll Do
  • Work across domains-for example, all plant sensor and historian data, or all lab and analytical results-including schema design, orchestration, reliability, and the contract it exposes to everyone downstream.
  • Design and evolve our fleet of pipelines that pull from messy industrial sources-sensors, lab systems, historians, imagery, and more-into our databases and warehouse.
  • Model time-series and analytical plant data for both human analysis and machine learning training, validation, and monitoring; own data quality, observability, and lineage in your domain.
  • Build the data architecture that feeds production ML-the training and monitoring layer-in partnership with the ML engineers who own the model-specific semantics.
  • Mentor earlier-career engineers and define the data contracts other teams build against.
  • Work the boundary with machine learning deliberately: you own the platform and the interface it exposes; ML engineers own the features and models built on top of it. The training and monitoring layer is shared ground you design together.
Desired Qualifications
  • 4-8+ years in data engineering or a closely related role.
  • Strong Python and SQL, with deep experience designing database and warehouse schemas, including time-series and/or analytical data.
  • Proven experience building reliable, orchestrated data pipelines and operating them in the cloud with containers and CI/CD.
  • Experience with data quality, observability, and lineage, and comfort with messy real-world sources-drifting sensors, malformed exports, and the quirks of industrial systems.
  • A self-starter comfortable in high-ambiguity environments, working directly with process engineers, ML engineers, and operations teams.
  • Bonus: experience feeding data to ML systems-training datasets, feature pipelines, model monitoring-or working with industrial, sensor, or historian data.
Why This Role
We own the projects, generate the data, and close the loop. Every facility we build makes the software smarter-and the next facility faster and cheaper.
Mining is one of the last major industrial sectors that hasn't been rebuilt with modern software. The opportunity here isn't a feature gap-it's entire workflows and systems that don't exist yet.
Your work will directly shape how critical minerals are produced at scale in the coming decades.
Our culture is built on three principles:
Extreme Ownership - We take full responsibility for outcomes, relentlessly driving toward solutions.
Engineer Out Requirements, then Automate - We simplify, optimize, and then automate for scale.
Share Your Legos - We collaborate openly, share knowledge, and empower each other to build bigger, better solutions.
Join us as we build the future of responsible mineral sourcing and supply.

Apply