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Overnight Databricks Data Engineer Jobs in Michigan

Stellantis

ICT Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

Data engineering is the practice of making the appropriate data available to various data consumers ... Snowflake, Databricks AWS, Azure, GCP). * Strong communication and stakeholder engagement skills.

Stellantis

Data Engineer - Supply Chain

Auburn Hills, MI · On-site

$108K - $130K/yr

The Data Engineer plays a critical role in enabling this vision by designing, building, and ... Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent

Stefanini

GCP Data Engineer

Dearborn, MI · On-site

$105K - $126K/yr

Stefanini is looking for a GCP Data Engineer, Dearborn, MI For quick apply, please reach out to ... AWS Glue, S3, Redshift, and Athena, Azure Data Factory and Databricks, Google BigQuery and Dataflow ...

Apex Systems

Data Engineer

Dearborn, MI · On-site

$105K - $126K/yr

Full-stack software engineering roles, who can develop all components of software including user ... Azure Data Factory, Databricks; Google BigQuery, Dataflow). * Strong understanding of data ...

Pwc

GenAI Python Systems Engineer-Manager

Detroit, MI

$99K - $232K/yr

Azure Solutions Architect Expert, Azure Data Engineer Associate, Snowflake Core, Snowflake Databricks Data Engineer Associate] is a plus - Proficient in Python and SQL - Experience with Docker and ...

Stryker

Senior Staff Data Engineer

Portage, MI · On-site +1

$153K - $255K/yr

Define and evolve enterprise data architecture patterns across Azure and Databricks environments ... Own end-to-end delivery of complex data engineering initiatives from requirements through ...

Stryker

Senior Staff Data Engineer

Portage, MI · On-site +1

$255K/yr

Define and evolve enterprise data architecture patterns across Azure and Databricks environments ... Own end-to-end delivery of complex data engineering initiatives from requirements through ...

Stryker

Senior Staff Data Engineer

Portage, MI · On-site

$153K - $255K/yr

Define and evolve enterprise data architecture patterns across Azure and Databricks environments ... Own end-to-end delivery of complex data engineering initiatives from requirements through ...

Stryker

Senior Staff Data Engineer

Portage, MI · On-site

$153K - $255K/yr

Define and evolve enterprise data architecture patterns across Azure and Databricks environments ... Own end-to-end delivery of complex data engineering initiatives from requirements through ...

Stellantis

Senior Data Engineer

Auburn Hills, MI · On-site

$100K - $136K/yr

This role helps engage business, engineering and ICT stakeholders around practical data needs and ... Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent

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Overnight Databricks Data Engineer information

What is an overnight Databricks data engineer?

An Overnight Databricks Data Engineer is a professional who works primarily during night shifts to manage, design, and maintain big data pipelines and workflows using Databricks, a cloud-based data analytics platform. Their responsibilities often include developing and optimizing data processing jobs, ensuring data quality, and troubleshooting issues that arise during overnight data operations. This role is critical for organizations that require 24/7 data processing, continuous ETL jobs, or real-time analytics. Working overnight may also involve monitoring automated systems, performing scheduled data loads, and collaborating with global teams to ensure data availability and reliability.

What skills and qualifications are needed to thrive as an overnight Databricks data engineer?

To thrive as an Overnight Databricks Data Engineer, you need strong proficiency in data engineering, Python or Scala programming, and experience with big data technologies, typically supported by a relevant degree in computer science or a related field. Familiarity with Databricks, Apache Spark, cloud platforms (such as AWS or Azure), and certifications like Databricks Certified Data Engineer are highly valued. Attention to detail, problem-solving, and effective communication are essential soft skills, especially for troubleshooting and collaborating across shifts. These competencies ensure reliable data pipeline management and efficient resolution of issues during off-hours, maintaining seamless business operations.

What unique challenges do overnight Databricks data engineers face, and how can they be addressed?

Overnight Databricks Data Engineers often work with limited real-time support, which can present challenges when troubleshooting urgent data pipeline issues or system outages. To address this, it’s essential to develop strong problem-solving skills, document processes thoroughly, and leverage automated monitoring and alerting tools. Additionally, close collaboration with daytime teams during handoff periods ensures continuity and minimizes disruptions. Building a habit of proactive communication and maintaining detailed logs helps the entire team resolve issues efficiently and maintain data quality.

What is the difference between Overnight Databricks Data Engineer vs Data Engineer?

AspectOvernight Databricks Data Engineer
Work EnvironmentPrimarily remote or on-site, working overnight shifts to support global data operations
CertificationsDatabricks certifications, cloud platform credentials (AWS, Azure), data engineering certifications
Tools & TechnologiesDatabricks platform, Spark, cloud services, SQL, Python, ETL tools
Industry UsageTech, finance, healthcare, retail with 24/7 data needs

While both roles focus on data engineering, the Overnight Databricks Data Engineer specializes in managing data pipelines on the Databricks platform during overnight shifts, often supporting global operations. A Data Engineer may work across various platforms and shifts, with broader responsibilities in data architecture and pipeline development. The overnight role emphasizes specific platform expertise and shift timing, catering to organizations with continuous data processing needs.

What are the most commonly searched types of Databricks Data Engineer jobs in Michigan?

The most popular types of Databricks Data Engineer jobs in Michigan are:

What cities in Michigan are hiring for Overnight Databricks Data Engineer jobs?

Cities in Michigan with the most Overnight Databricks Data Engineer job openings:

Overnight Databricks Data Engineer jobs near you

Senior Data Engineer

Stellantis

Auburn Hills, MI • On-site

$100K - $136K/yr

Full-time

Re-posted 5 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

14th of 44 rated automakers

Job description

About the Role
Join the Supply Chain AI Hub as a Senior Data Engineer helping turn AI ambition into reliable data foundations and delivery-ready assets. This role helps engage business, engineering and ICT stakeholders around practical data needs and constraints, scale AI delivery through stronger data models, pipelines, integration pathways, quality routines and traceability, and pioneer more robust data-engineering practices that make solutions easier to trust, operate and industrialize.
Your Missions:
Data Modelling, Pipelines & Reuse:
  • Design, improve or govern selected data models, transformation logic and pipeline components that support AI and analytics use cases
  • Promote maintainable structures, reusable components and clear lineage across transformations where relevant
  • Support delivery teams with practical data-engineering discipline rather than one-off technical builds

Platform, Integration & Traceability:
  • Clarify selected source-to-platform pathways, integration dependencies and technical constraints affecting delivery
  • Help maintain visibility on traceability, handoffs and access conditions across Supply Chain
  • Work with ICT and engineering stakeholders to keep the build path practical and scalable

Data Quality, Certification & Governance Support:
  • Contribute to selected quality checks, certification routines, governance expectations or compliance-related traceability needs depending on the scope assigned
  • Help surface structural data issues, documentation gaps or control weaknesses that affect deployment readiness
  • Support a trusted delivery environment by making data assets more visible, understandable and supportable

Your Profile:
  • Strong data-engineering experience in modern enterprise environments, with depth in some combination of data modelling, pipelines, integration, quality, lineage or governance-related topics
  • Able to operate across business needs, technical constraints and delivery realities
  • Strong SQL and practical understanding of data structures, transformations, traceability and controlled delivery environments
  • Comfortable working with multiple stakeholders across architecture, data, engineering and governance topics
  • Structured, pragmatic and able to take ownership of a defined subset of a broader senior data-engineering scope

Skills You'll Grow:
  • Broader exposure across the different building blocks that make AI-ready data operational at scale
  • Experience working at the intersection of data engineering, integration, quality and delivery governance
  • Opportunity to deepen expertise in a specific component while contributing to a wider AI data foundation agenda

Why Join / Impact:
  • Work on data-engineering challenges directly tied to real AI deployment in Supply Chain
  • Join a role broad enough to offer variety, while still allowing focused ownership on a defined perimeter
  • Help strengthen the data foundations that make scalable AI delivery possible

Basic Qualifications:
  • Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or related field
  • 8 years of experience in data engineering or data platforms
  • Previous Supply Chain experience
  • Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent
  • Experience with data pipelines, integration, semantic, lineage, architecture and platform environments
  • Enterprise-scale data transformation and delivery experience
  • Ability to collaborate effectively with analytics, AI, and software engineering teams

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