The Role
Designs, builds, and optimizes scalable batch and real-time data pipelines using Apache Spark and distributed data technologies. Manages cloud infrastructure with Terraform, maintains GitHub Actions CI/CD automation, and applies rigorous software engineering, testing, documentation, and code review practices. The role requires advanced SQL, JVM-based programming, Python, Apache Flink, cloud data services, data modeling, ETL, streaming, and data quality expertise. Collaborates with stakeholders to translate business requirements into technical specifications.
Summary Generated by Built In
Job Summary & Responsibilities
The AI & Data Analytics Team is looking for a Senior Data Engineer to join our team. In this role, you will be responsible for designing, building, and optimizing robust data pipelines that process massive datasets in both batch and real-time. You will work at the intersection of software engineering and data science, ensuring that our data architecture is scalable, reliable, and follows industry best practices.
Priorities can change in a fast-paced environment like ours, so this role includes, but is not limited to, the following responsibilities:
- Pipeline Development: Design and implement complex data processing pipelines using Apache Spark.
- Architectural Leadership: Build scalable, distributed systems that handle high-throughput data streams and large-scale batch processing.
- Infrastructure as Code: Manage and provision cloud infrastructure using Terraform.
- CI/CD & Automation: Streamline development workflows by implementing and maintaining GitHub Actions for automated testing and deployment.
- Code Quality: Uphold rigorous software engineering standards, including comprehensive unit/integration testing, code reviews, and maintainable documentation.
- Collaboration: Work closely with stakeholders to translate business requirements into technical specifications.
Basic Qualifications:
- Bachelors degree in Computer Science, Engineering, Mathematics, or a related technical discipline
- A minimum of 5 years of experience in the data engineering and software development life cycle. Including:
- A minimum of 4 years of hands-on experience in building and maintaining production data applications, current experience in both relational and columnar data stores.
- A minimum of 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 working with Big Data platforms (i.e., Spark, Google Big Query, Azure, AWS S3, etc.)
- Familiarity with time series database, data streaming applications, event driven architectures, Kafka, Flink, and more
- Experience with workflow management engines (i.e., Airflow, Luigi, Azure Data Factory, etc.)
- Experience with designing and implementing real-time pipelines
- Experience with data quality and validation
- Experience with API design
- Distributed Computing: Deep expertise in Apache Spark (Core, SQL, and Structured Streaming).
- Programming Mastery: Strong proficiency in Scala or Java. You should be comfortable building production-grade applications in a JVM-based environment.
- SQL Proficiency: Advanced knowledge of SQL for data transformation, analysis, and performance tuning.
- DevOps & Tools: Hands-on experience with Terraform for infrastructure management and GitHub Actions for CI/CD pipelines.
- Software Engineering Foundation: Solid understanding of data structures, algorithms, and design patterns. Experience applying "Clean Code" principles to data engineering.
- Stream Processing: Experience with Apache Flink for low-latency stream processing.
- Scripting: Proficiency in Python for automation, data analysis, or scripting.
- Cloud Platforms: Experience with AWS, Azure, or GCP data services (e.g., EMR, Glue, Databricks).
- Data Modeling: Familiarity with dimensional modeling, Lakehouse architectures (Delta Lake, Iceberg), or NoSQL databases.
Preferred Qualifications:
- 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 with custom ETL design, implementation and maintenance
- Comprehensive experience designing, implementing, and iterating data pipelines using Big Data technologies
- Certification in AWS or other cloud providers
- Experience with Databricks notebook workflows
- Experience with Terraform
Skills Required
- 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 lifecycle
- At least 4 years of hands-on experience building and maintaining production data applications
- Current experience with relational and columnar data stores
- At least 4 years of hands-on experience with AWS cloud services
- Comprehensive experience with Python, Java, or Rust
- Experience with Big Data platforms such as Spark, 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, SQL, and Structured Streaming
- Strong proficiency in Scala or Java and production-grade JVM application development
- Advanced SQL knowledge for data transformation, analysis, and performance tuning
- Hands-on experience with Terraform for infrastructure management
- Hands-on experience with GitHub Actions for CI/CD pipelines
- Understanding of data structures, algorithms, design patterns, and Clean Code principles
- 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, Delta Lake, Iceberg, or NoSQL databases
- Knowledge of relational database concepts, data architecture, operational data stores, interface processes, multidimensional modeling, master data management, and data manipulation
- Expertise in custom ETL design, implementation, and maintenance
- Experience designing, implementing, and iterating data pipelines using Big Data technologies
- AWS or other cloud-provider certification
- Experience with Databricks notebook workflows
- Experience with Terraform
Stellantis Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Stellantis and has not been reviewed or approved by Stellantis.
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Healthcare Strength — Benefits are described as broad with strong medical, dental, and vision coverage, plus life insurance, disability, wellness resources, and employee assistance programs. Coverage breadth appears especially robust for full-time roles in North America.
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Retirement Support — Retirement savings are highlighted through 401(k) matching and company contributions in eligible roles, alongside attractive pension and retirement terms for many union-represented manufacturing employees. These elements are positioned as a core strength of the overall package.
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Equity Value & Accessibility — Employee share ownership is emphasized, with the share-purchase program expanded to nearly the entire workforce and broad participation reported. This accessibility creates an additional wealth-building channel beyond base pay.
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The Company
What We Do
Our storied and iconic brands embody the passion of their visionary founders and today’s customers in their innovative products and services: they include Abarth, Alfa Romeo, Chrysler, Citroën, Dodge, DS Automobiles, Fiat, Jeep®, Lancia, Maserati, Opel, Peugeot, Ram, Vauxhall and mobility brands Free2move and Leasys. Powered by our diversity, we lead the way the world moves – aspiring to become the greatest sustainable mobility tech company, not the biggest, while creating added value for all stakeholders as well as the communities in which we operate.
