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New Grad Data Engineer Jobs in Michigan (NOW HIRING)

Senior Data Engineer

Wyoming, MI · Hybrid

$96K - $130K/yr

The Senior Data Engineer guides the development of GFS' Data Platform consisting of the data lake ... Mentor to other team members in the appropriate use and application of new and existing tools and ...

Senior Data Engineer

Grand Rapids, MI · On-site

$97K - $121K/yr

Research and promote new tools and techniques to shape the future of the data platform, and build ... Azure DevOps). Minimum Qualifications * At least 3 years of full-time experience in US as Data ...

New

Senior Data Engineer

Grand Rapids, MI · On-site

$97K - $121K/yr

Research and promote new tools and techniques to shape the future of the data platform, and build ... Azure DevOps). Minimum Qualifications * At least 3 years of full-time experience in US as Data ...

Data Engineer (with NIKE exp.)

Dearborn, MI · On-site

$105K - $126K/yr

Damco Solutions is seeking a Data Engineer with experience in big data technologies. The role ... new technology/toolset • Learning attitude and flexible with project and timings • Good ...

Google Cloud Platform Data Engineer

Dearborn, MI · On-site

$105K - $126K/yr

Continuously improve performance, optimize applications, and implement new technologies to maximize ... data engineering or a related role, with a significant focus on large-scale data systems.Expert ...

$103K - $124K/yr

Work collaboratively with internal stakeholders to explore new data sources and translate them into ... Collaborate with FPS Champions, Engineering, Operations, and IT to align solutions with ...

Showing results 41-60

New Grad Data Engineer information

What is a new grad data engineer?

A New Grad Data Engineer is an entry-level role for recent graduates who focus on designing, building, and maintaining data pipelines and infrastructure. They work with databases, ETL (Extract, Transform, Load) processes, and big data technologies to ensure efficient data flow and storage. Typically, they collaborate with data scientists, analysts, and software engineers to support data-driven decision-making. This role requires knowledge of SQL, Python, and cloud platforms, along with problem-solving and analytical skills. It is an excellent opportunity to gain hands-on experience in data engineering while learning industry best practices.

What does a new grad data engineer do?

As a New Grad Data Engineer, your day often involves writing and optimizing code for data pipelines, cleaning and transforming data, and troubleshooting any issues that arise. You’ll work closely with senior data engineers, data scientists, and sometimes business stakeholders to understand data requirements and deliver reliable solutions. Many entry-level roles emphasize learning and professional growth, so you can expect regular mentorship, code reviews, and opportunities to work on small components of larger projects. Over time, you’ll take on more complex responsibilities and contribute to the overall data infrastructure of your organization.

What are the key skills and qualifications needed to thrive as a new grad data engineer?

To thrive as a New Grad Data Engineer, you need a strong grasp of programming languages like Python or SQL, a background in computer science or a related field, and an understanding of data modeling and database concepts. Familiarity with data engineering tools such as ETL pipelines, cloud platforms like AWS or Azure, and certifications in these areas can be beneficial. Strong problem-solving skills, effective communication, and a willingness to learn are valuable soft skills for this position. These abilities ensure you can effectively handle complex data tasks, collaborate with technical teams, and adapt to evolving technologies in a fast-paced environment.

Are data engineers still in 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.

Can I get a new grad data engineer job with no experience?

Securing a new grad data engineer position without experience is possible if you have relevant skills in programming, databases, and data processing tools like SQL, Python, or Spark. Entry-level roles often focus on potential and foundational knowledge, and internships or certifications can strengthen your application.

What job categories do people searching New Grad Data Engineer jobs in Michigan look for?

The top searched job categories for New Grad Data Engineer jobs in Michigan are:

What cities in Michigan are hiring for New Grad Data Engineer jobs?

Cities in Michigan with the most New Grad Data Engineer job openings:

Infographic showing various New Grad Data Engineer job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 13% Part Time, 2% Temporary, 3% Contract, and 1% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Staff Data Engineer - Core Data Pipelines

KODE Labs

Detroit, MI • On-site

$140 - $190/hr

Other

PTO

Posted 14 days ago


Job description

Our team at KODE Labs is looking for a Staff Data Engineer to lead the technical foundations behind our core data pipelines, data alignment, quality, lineage, and observability standards.

This is a highly cross-functional technical leadership role. You’ll build shared data foundations while defining the patterns and standards used by teams across Integrations, Digital Twin, Data Platform, Fault Detection & Diagnostics (FDD), Energy, Analytics, APIs, and AI Agents.

You won’t own every product-specific pipeline or validation rule. Instead, you’ll establish the foundations that allow teams across KODE to build on trusted, scalable, observable, and consistently modeled data.

What You Will Do

Design, build, and evolve core data pipelines and datasets that serve as shared foundations across KODE OS.

Define reusable patterns for aligning IoT, BMS, operational, asset, meter, work order, schedule, weather, occupancy, and time-series data with our Digital Twin.

Establish standards for the full data lifecycle — from raw ingestion through alignment, cleaning, modeling, serving, and consumption.

Build shared frameworks for data validation, normalization, lineage, observability, and readiness .

Define common approaches to data quality, including quality flags, confidence scores, validation signals, and product-readiness checks.

Establish time-series standards covering timestamps, time zones, expected frequencies, gaps, late-arriving data, duplicates, and standard aggregation grains.

Define and evolve data contracts, schema standards, and versioning practices across data producers and consumers.

Build observability foundations that surface issues with freshness, coverage, schema changes, data quality, pipeline failures, and data drift before they impact downstream products.

Develop reusable mapping and alignment patterns that make onboarding new integrations and data sources more consistent and scalable.

Partner closely with Integrations and Digital Twin engineers to ensure required identifiers, metadata, entity relationships, point classifications, and semantic alignment are reliable.

Work with FDD, Energy, Analytics, API, and AI teams to ensure downstream products consume data with clear quality, freshness, lineage, semantic context, and readiness indicators.

Review new data models, schemas, pipelines, mappings, and cross-team data flows to ensure they follow shared engineering standards.

Provide technical leadership across teams, helping engineers make strong architectural decisions and avoid solving the same data problems in different ways.

Mentor engineers and raise the bar for data engineering practices, system design, documentation, reliability, and maintainability across KODE.

Requirements
  • Staff-level experience in data engineering or data platform engineering, with a track record of designing scalable systems and leading technical initiatives across teams.

Strong experience building and operating core data pipelines, including batch and/or streaming architectures.

Deep understanding of data modeling, data quality, contracts, lineage, observability, and schema evolution.

Strong proficiency in SQL and Python.

Experience working with IoT, telemetry, time-series, operational, or other high-volume event data.

Experience with Digital Twins, ontologies, semantic models, metadata modeling, or domain-driven data structures.

Hands‑on experience with technologies such as Kafka, Flink, Spark, Airflow, dbt, BigQuery, ClickHouse, PostgreSQL, or comparable tools.

Strong systems thinking, with the ability to design data flows from ingestion and alignment through modeling and downstream consumption.

Proven ability to set technical standards, influence architecture across teams, and mentor engineers without directly owning every implementation.

WHAT WE OFFER:

Competitive salary based on experience

Discretionary Bonus Program

Career Development Program and opportunities to grow within the company

Flexible Paid Time Off

Dynamic team and challenging projects

Custom‑tailored onboarding experience

Welcoming and friendly work environment

Social events and team activities

JOIN THE TEAM

KODE Labs is a real estate technology company founded in 2017 with a mission to change the way people, buildings, and systems operate. Headquartered in Detroit, Michigan, we are a driving force behind the adoption of smart building technology. To scale our presence across numerous cities and countries, we depend on our team of talented, ambitious people who go above and beyond to create value for our clients.

When you join the KODE Labs team you can create your own career. Whether you have years of experience or are just starting, we help you realize your full potential and achieve your goals.

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