Data Engineer – Mobilisights (Stellantis Data-as-a-Service)

About Mobilisights

Mobilisights, a business unit of Stellantis, is building the future of connected vehicle data and real-time mobility intelligence.

With millions of connected cars and devices generating high-volume sensor data, we are creating a Data-as-a-Service (DaaS) platform that powers innovative applications for consumers, enterprises, and mobility ecosystems worldwide.

We operate with the scale of one of the world's largest automotive groups - Stellantis (Jeep, Fiat, Maserati, Peugeot, and more) - and the mindset of a startup. That means rapid innovation, cloud-native engineering, and real-time data at massive scale.

We are building the foundation of a smarter world powered by streaming, real-time, cloud-based data platforms.

Role Overview – Data Engineer (Streaming / Cloud / Big Data)

We are looking for a Senior Data Engineer with strong experience building and operating large-scale, cloud-native, real-time data streaming systems.

In this role, you will design and build the core data infrastructure that powers Mobilisights' data products - handling massive, real-time vehicle and IoT data streams with a focus on scalability, reliability, and low latency.

This is a hands-on engineering role focused on data platforms, streaming pipelines, AWS cloud architecture, and production-grade data systems.

Key Responsibilities

Data Platform & Architecture

. Design and build a scalable, cloud-native data platform capable of ingesting, storing, processing, and streaming massive real-time datasets

. Architect systems that support high-volume sensor data ingestion and near real-time processing

. Enable fast and reliable development of data products and analytics services

Streaming Data Engineering

Build and maintain real-time streaming data pipelines

Implement data processing workflows using modern streaming technologies such as:

Apache Kafka

Apache Spark / PySpark

Apache Flink

AWS Kinesis

Ensure low-latency, high-throughput data delivery for downstream applications

Cloud Engineering (AWS Focus)

Develop and optimize AWS-based data architectures

Work with services such as:

AWS EMR

AWS EKS

AWS Lambda

AWS MSK (Managed Kafka)

Implement Infrastructure as Code (IaC) and automated deployment pipelines

Support secure, scalable, and production-ready cloud infrastructure

Data Reliability & Operations

Ensure data consistency, reliability, and fault tolerance across pipelines

Build and maintain:

Monitoring systems

Logging frameworks

Alerting and observability tools

Implement data quality checks and data loss detection mechanisms

Participate in on-call production support and incident troubleshooting

Engineering Excellence

Contribute to operational best practices including:

Restartability of pipelines

Error handling strategies

System resilience and recovery

Work closely with cross-functional teams to deliver production-grade data system

Requirements:

Basic Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or related field

A minimum of 8 years of experience in software engineering or data engineering roles

Strong experience building and operating cloud-native, production-grade data streaming systems

Hands-on experience with real-time data architectures emphasizing:

Scalability

Low latency

High availability

Data quality and privacy

Required or strong experience with:

Streaming & big data technologies: Kafka, Spark, Flink, Kinesis

Cloud platforms: AWS (EMR, EKS, Lambda, MSK)

Data processing frameworks: Spark / PySpark

Data architecture concepts: data lakes / lakehouse (Databricks experience a plus)

SQL and relational database fundamentals

Infrastructure as Code (Terraform or similar preferred)

CI/CD and deployment automation in cloud environments

Preferred Qualifications:

Experience with Databricks Lakehouse Platform

Programming experience in Scala, Java, or Python

Experience building data observability, monitoring, and data quality frameworks

Experience in high-scale IoT, automotive, or sensor-based data systems

Mindset & Soft Skills:

Curiosity and passion for learning new technologies

Strong bias toward action and problem solving

Ability to operate in a fast-moving, startup-like environment within a large global organization

Strong ownership mindset with ability to support production systems

High Level Edits:

Reorganized into standard job posting structure (co overview, role overview, responsibilities, qualifications, technical skills, nice to have)

Clarified the role into a more “real job description”

Shifted from marketing heavy language to candidate relevant messaging

Improved scan ability and readability

Standardized technical expectations

Aligned seniority and expectations (this is a senior data engineer role minimum of 8 years)

Expanded keywords that ATS systems and recruiters filters for