Data Engineer

Stellantis•Auburn Hills, MI

About The Position

The AI & Data Analytics Team is seeking a Senior Data Engineer to design, build, and optimize robust data pipelines that process large datasets in batch and in real time. The role sits at the intersection of software engineering and data science, with responsibility for scalable, reliable data architecture that follows industry best practices. Priorities may change in a fast-paced environment, and the listed responsibilities are not exhaustive.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline.
At least 5 years of experience in data engineering and the software development life cycle.
At least 4 years of hands-on experience building and maintaining production data applications, with current experience in relational and columnar data stores.
At least 4 years of hands-on experience working with AWS cloud services.
Comprehensive experience with one or more programming languages such as Python, Java, or Rust.
Comprehensive experience with big data platforms such as Spark, Google BigQuery, Azure, or AWS S3.
Familiarity with time-series databases, data streaming applications, event-driven architectures, Kafka, and Flink.
Experience with workflow management engines such as Airflow, Luigi, or Azure Data Factory.
Experience designing and implementing real-time pipelines.
Experience with data quality and validation.
Experience with API design.
Deep expertise in Apache Spark Core, Spark SQL, and Structured Streaming.
Strong proficiency in Scala or Java and ability to build production-grade applications in a JVM-based environment.
Advanced SQL knowledge for data transformation, analysis, and performance tuning.
Hands-on experience with Terraform for infrastructure management and GitHub Actions for CI/CD pipelines.
Solid understanding of data structures, algorithms, and design patterns, and experience applying Clean Code principles to data engineering.
Experience with Apache Flink for low-latency stream processing.
Proficiency in Python for automation, data analysis, or scripting.
Experience with AWS, Azure, or GCP data services such as EMR, Glue, or Databricks.
Familiarity with dimensional modeling, Lakehouse architectures such as Delta Lake or Iceberg, or NoSQL databases.

Nice To Haves

Comprehensive knowledge of relational database concepts, including data architecture, operational data stores, interface processes, multidimensional modeling, master data management, and data manipulation.
Expert knowledge and experience in custom ETL design, implementation, and maintenance.
Comprehensive experience designing, implementing, and iterating data pipelines using big data technologies.
Certification in AWS or another cloud provider.
Experience with Databricks notebook workflows.
Experience with Terraform.

Responsibilities

Design and implement complex data processing pipelines using Apache Spark.
Build scalable distributed systems for high-throughput data streams and large-scale batch processing.
Manage and provision cloud infrastructure using Terraform.
Implement and maintain GitHub Actions for automated testing and deployment.
Maintain high software engineering standards through unit and integration testing, code reviews, and documentation.
Collaborate with stakeholders to translate business requirements into technical specifications.

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