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Contract Data Engineering Jobs in Michigan (NOW HIRING)

CertifID

Senior Data Engineer

Grand Rapids, MI

$101K - $138K/yr

... contract validation, deployment gates, and cost optimization and performance tuning as ongoing practice, not one-time projects What We're Looking For Experience * 6+ years in data engineering with ...

CertifID

Senior Data Engineer

Grand Rapids, MI · On-site +1

$101K - $138K/yr

... contract validation, deployment gates, and cost optimization and performance tuning as ongoing practice, not one-time projects What We're Looking For Experience * 6+ years in data engineering with ...

ARKA Group

Contracts Manager (TS/CSI) {S}

Ypsilanti, MI · Hybrid

$80K - $107K/yr

Legal, Business Development, Program Management, Engineering, Finance, Supply Chain etc.) in ... Support Contract data input/entry into SAP Required Qualifications: * Minimum of 5 years relevant ...

ARKA Group

Contracts Manager (TS/CSI) {S}

Ypsilanti, MI · On-site

$80K - $107K/yr

Legal, Business Development, Program Management, Engineering, Finance, Supply Chain etc.) in ... Support Contract data input/entry into SAP Required Qualifications: * Minimum of 5 years relevant ...

Trilyon, Inc.

Sr. Data Engineer - Lansing, MI

Lansing, MI · On-site

$116K - $139K/yr

We partner with top companies to deliver high-quality talent in engineering, IT, and emerging ... Lansing, MI (Hybrid - Onsite 2 days/week, REQUIRED) Duration: 1 year contract with possible ...

Kiongroup

Senior AI Context Engineer

Grand Rapids, MI · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data ... Develop and enforce data contracts, metadata standards, and semantic governance frameworks across ...

Kiongroup

Senior AI Context Engineer

Grand Rapids, MI · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data ... Develop and enforce data contracts, metadata standards, and semantic governance frameworks across ...

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Contract Data Engineering information

What is the difference between Contract Data Engineering vs Data Analyst?

AspectContract Data EngineeringData Analyst
Required SkillsSQL, Python, ETL, cloud platforms, data pipeline developmentSQL, Excel, data visualization, reporting tools
Work EnvironmentProject-based, technical teams, cloud or on-premises infrastructureBusiness units, reporting teams, often in office or remote
Industry UsageTech, finance, healthcare, retailMarketing, finance, healthcare, retail

Contract Data Engineers focus on building and maintaining data pipelines and infrastructure, requiring technical skills in programming and cloud platforms. Data Analysts interpret data, create reports, and visualize insights, often using different tools. While both roles work with data, Contract Data Engineering is more technical and infrastructure-oriented, whereas Data Analysts focus on data interpretation and business insights.

What is the salary of a freelance data engineer?

The salary of a freelance data engineer varies widely based on experience, project complexity, and location, but typically ranges from $50 to $150 per hour. Experienced professionals with skills in cloud platforms, data pipelines, and programming languages like Python or SQL tend to command higher rates. Freelancers often set their rates based on project scope and client requirements.

What engineers make $500,000?

Senior data engineers, especially those with extensive experience, specialized skills in cloud platforms, and expertise in big data tools, can earn $500,000 or more annually. High compensation is often associated with leadership roles, consulting, or working in high-demand industries such as finance or technology. Achieving this level typically requires advanced certifications, a strong track record, and sometimes equity or performance bonuses.

Is AI replacing data engineers?

AI is automating certain tasks within data engineering, such as data cleaning and pipeline management, but it does not replace the need for data engineers. Data engineers are essential for designing, building, and maintaining complex data systems, and their skills in programming, database management, and cloud platforms remain in high demand. AI tools serve as complements that enhance efficiency rather than substitutes for the core responsibilities of data engineers.

What is contract data engineering?

Contract data engineering refers to hiring data engineers on a temporary or project basis, rather than as full-time employees. Contract data engineers are responsible for designing, building, and maintaining data pipelines, databases, and other infrastructure to support data analytics and business needs. Companies often hire contract data engineers to handle specific projects, scale up teams quickly, or bring in specialized skills for a limited time. This arrangement offers flexibility for both the company and the engineer, and is common in industries with fluctuating data workloads or short-term projects.

Is contract engineering a good career?

Contract data engineering involves short-term or project-based work focused on designing, building, and maintaining data pipelines and systems. It offers flexibility and diverse experience but may lack long-term job security and benefits compared to permanent roles. Success in this career often depends on technical skills, certifications, and the ability to adapt to different environments.

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

To thrive as a Contract Data Engineer, you need strong proficiency in data modeling, ETL processes, and programming languages such as Python or SQL, often supported by a degree in computer science or a related field. Familiarity with big data platforms (e.g., Hadoop, Spark), cloud services (AWS, Azure, GCP), and relevant certifications like Google Cloud Professional Data Engineer are typically required. Excellent problem-solving, adaptability, and effective communication are crucial soft skills in this role. These competencies enable efficient project delivery, seamless collaboration with stakeholders, and the ability to quickly adapt to new technical environments and client requirements.

What are some common challenges faced by contract data engineers and how can they be addressed?

Contract data engineers often face the challenge of quickly familiarizing themselves with a company's existing data infrastructure and processes. Since contracts are typically short-term, there is limited time to onboard, understand unique data pipelines, and build relationships with stakeholders. To address this, successful contract data engineers proactively communicate with team members, document their work thoroughly, and leverage their prior experience with a variety of tools and platforms. Flexibility and strong problem-solving skills are essential for adapting to new environments and delivering results efficiently.
What are the most commonly searched types of Data Engineering jobs in Michigan? The most popular types of Data Engineering jobs in Michigan are:
What are popular job titles related to Contract Data Engineering jobs in Michigan? For Contract Data Engineering jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Contract Data Engineering jobs in Michigan look for? The top searched job categories for Contract Data Engineering jobs in Michigan are:
What cities in Michigan are hiring for Contract Data Engineering jobs? Cities in Michigan with the most Contract Data Engineering job openings:
Contract Data Engineering jobs near you
Senior Data Engineer

Senior Data Engineer

CertifID

Grand Rapids, MI

$101K - $138K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 10 days ago

Job description

Cybercrime is rising, reaching record highs in 2024. According to the FBI's IC3 report total losses exceeded $16 billion. With investment fraud and BEC scams at the forefront, the message is clear: the real estate sector remains a lucrative target for cybercriminals. At CertifID, we take this threat seriously and provide a secure platform that verifies the identities of parties involved in transactions, authenticates wire transfer instructions, and detects potential fraud attempts. Our technology is designed to mitigate risks and ensure that every transaction is conducted with confidence and peace of mind.
 
We know we couldn't take on this challenge without our incredible team. We have been recognized as one of the Best Startups to Work for in Austin, made the Inc. 5000 list, and won Best Culture by Purpose Jobs two years in a row. We are guided by our core values and our vision of a world without wire fraud. We offer a dynamic work environment where you can contribute to meaningful impact and be part of a team dedicated to enhancing security and fighting fraud.

CertifID is the wire fraud prevention platform protecting real estate closings. Every transaction we secure generates data: identity signals, verification events, behavioral patterns, payment flows. That data is how we detect fraud, how our customers measure risk, and how the business operates. You will own the systems that make it trustworthy, fast, and useful.


What You'll Do

Data platform and pipeline engineering

  • Design, build, and operate the core data infrastructure: data lake, warehouse, orchestration, observability, and governance, using declarative configuration and infrastructure as code (Terraform or equivalent) so the platform is reproducible and auditable

  • Partner with platform and domain teams to design ingestion pipelines and implement declarative configuration for data sources across the stack

  • Architect the transformation layer: dimensional models, aggregation strategies, and incremental materialization patterns that balance query performance against pipeline cost at scale

  • Own streaming and near-real-time data flows for fraud signal propagation, transaction status events, and verification webhooks, with the reliability expectations those require

  • Build for scale: partition strategies, clustering, late-arriving data handling, and backfill patterns that hold up when data volume doubles

Business outcome ownership

  • Own the source-of-truth models for the metrics the business runs on: ARR, NRR, churn, transaction volume, fraud detection rates, customer health scores, and operational throughput

  • Make the numbers defensible: when a business leader challenges a metric, you can walk them through exactly how it is calculated, what is excluded, and why

  • Partner with Product, Finance, CS, and GTM to translate business questions into data models and help teams measure what actually matters 

Engineering craft and standards

  • Write production-grade Python and SQL: modular, tested, version-controlled, and reviewable by someone who was not in the room when you wrote it

  • Implement CI/CD pipelines for data systems: automated testing, schema change detection, data contract validation, deployment gates, and cost optimization and performance tuning as ongoing practice, not one-time projects

What We're Looking For

Experience

  • 6+ years in data engineering with primary, end-to-end ownership of a production data platform, not a supporting role on a large team

  • Direct experience designing and operating streaming or near-real-time pipelines (Kafka, Kinesis, Pub/Sub, Flink, or equivalent) at production scale, including debugging failures under load

  • Hands-on production experience with cloud-based data platforms (Snowflake, BigQuery, Redshift, Databricks, or equivalent) and a production-grade orchestrator (Airflow, Dagster, Prefect, or equivalent)

Technical depth

  • Expert SQL and distributed systems: window functions, recursive CTEs, query plan analysis, query concurrency management, and optimization strategies that go beyond adding an index

  • Strong Python for data engineering: production-quality pipeline code with error handling, idempotency, retry logic, and test coverage; Go is a meaningful plus

  • Dimensional modeling mastery: you understand the tradeoffs between normalized and denormalized designs, when SCDs are the right tool, and how incremental strategies affect downstream query semantics

  • Event-driven architecture fundamentals: exactly-once semantics, consumer group management, backpressure handling, offset management, and the operational realities of keeping a streaming pipeline healthy

  • Warehouse internals: clustering keys, materialized views, partition pruning, and cost optimization strategies that keep query costs from compounding as data volume grows

What Sets You Apart

  • You instrument, measure, and verify that your work produced the outcome it was supposed to

  • You make architectural decisions independently, communicate outwardly, and document the reasoning so the decision survives you

  • You have joined teams where the data was a mess, and you shipped before the situation was fully resolved, because waiting for perfection was not an option

What We Offer
  • Flexible vacation
  • 12 company-paid holidays
  • 10 paid sick days
  • No work on your birthday
  • Health, dental, and vision Insurance (including a $0 option)
  • 401(k) with matching, and no waiting period
  • Equity
  • Life insurance
  • Generous parental paid leave
  • Wellness reimbursement of $300/year
  • Remote worker reimbursement of $300/year
  • Professional development reimbursement
  • Competitive pay
  • An award-winning culture
Not sure if you check all the boxes? Apply anyway! 
 
We know that great talent comes in many forms, and we value potential just as much as experience. If you're excited about this role and believe you can grow into it, we'd love to hear from you. We're looking for people who are eager to learn, adapt, and solve challenges-so if that sounds like you, don't let a checklist hold you back!
 
Change doesn't happen overnight, and the same goes for us here at CertifID. We evolve collectively and individually as we grow by leaning into the core values that define us. As we grow, we embody GRIT-collectively and individually-to raise the bar and influence outcomes in everything we do. Guard the Customer - Raise the Bar - Influence Outcomes - Teamwork Wins
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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