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Data Engineer – Python, SQL, Spark, Databricks – Hybrid – Ann Arbor, MI
Data Engineer – Python, SQL, Spark, Databricks – Hybrid – Ann Arbor, MIConexess Group, LLC  • Ann Arbor, MI
Data Engineer – Python, SQL, Spark, Databricks – Hybrid – Ann Arbor, MI

Data Engineer – Python, SQL, Spark, Databricks – Hybrid – Ann Arbor, MI

Conexess Group, LLC • Ann Arbor, MI
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Job Title : Data Engineer – Python, SQL, Spark, Databricks – Hybrid – Ann Arbor, MI

Location : Ann Arbor, MI – Hybrid – Monday – Thursday On-Site

About the Role :

We are seeking a skilled Data Engineer to join our client's cloud transformation initiative, focused on building and optimizing large-scale data pipelines in Databricks . You will play a key role in enabling real-time analytics and machine learning solutions that directly support millions of daily transactions and customer interactions worldwide.

Key Responsibilities :

  • Design, build, and maintain scalable data pipelines using Python , SQL , and Apache Spark within Databricks .
  • Work closely with Data Architects, Data Scientists, and Analysts to ensure data is accurate, available, and high-performing.
  • Integrate diverse data sources into a centralized cloud platform on Microsoft Azure .
  • Implement best practices for data ingestion, transformation, storage, and retrieval.
  • Optimize data workflows for large-scale processing and near-real-time analytics.
  • Ensure compliance with data governance, quality, and security standards.

Required Qualifications :

  • years of experience in data engineering and pipeline development.
  • Proficiency in Python , SQL , and Apache Spark .
  • Hands-on experience with Databricks (Databricks certification required).
  • Strong understanding of data modeling, ETL / ELT processes, and cloud data architectures.
  • Experience working with high-volume, complex data environments.

    • Preferred Qualifications :

  • Experience with Azure Data Factory, Azure Synapse Analytics, or Azure Data Lake Storage.
  • Familiarity with real-time data streaming tools (Kafka, Event Hubs, etc.).
  • Exposure to AI / ML data preparation workflows.
  • Background in high-transaction industries such as retail, e-commerce, or QSR.
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