Data Engineer | AWS Big Data & Vehicle Analytics

Data Engineer AWS Big Data & Vehicle Analytics

Hiire is supporting a global enterprise technology organization in the search for multiple Data Engineers to join a growing data platform team in Lisbon.

This role focuses on designing and operating production-grade AWS data pipelines that process vehicle telemetry and signal data at massive scale powering analytics operational insights and decision-making across international business teams.

The environment is particularly well suited to engineers who enjoy distributed systems large-scale data processing cloud-native architectures and solving technically complex real-world challenges.

About the Project

Youll work on a mature data platform responsible for ingesting and processing multi-terabyte datasets generated from connected vehicle systems.

The platform transforms raw telemetry signals and operational data into structured analytics-ready datasets used across engineering production and business domains.

This is a technically challenging environment combining:

• Distributed data processing

• Large-scale cloud infrastructure

• Data engineering best practices

• Vehicle and telemetry data processing

• International collaboration with cross-functional teams

What Youll Do

• Design build and maintain scalable AWS-based data pipeline architectures

• Process and decode large-scale vehicle telemetry and signal datasets

• Build ETL/ELT pipelines using Python PySpark SQL and AWS big-data services

• Transform raw operational data into analytics-ready datasets

• Build analytics tooling and data workflows that support business decision-making

• Collaborate closely with international stakeholders and engineering teams

• Contribute to architecture decisions and platform evolution

• Ensure reliability scalability and performance across large-scale data environments

What Were Looking For

• Hands-on experience as a Data Engineer or Software Engineer working with data-intensive systems

• Strong production-level Python skills

• Experience with PySpark or Apache Spark ecosystems

  • Academic exposure may be sufficient for junior profiles

  • Production-level distributed processing experience expected for senior candidates

• Strong SQL skills including query optimization and data modeling

• Experience building and operating ETL pipelines

• Hands-on experience with AWS services including:

  • S3

  • Glue

  • Athena

  • Lambda

  • IAM

• Understanding of Data Lake Data Warehouse and RDS architectures

• Strong communication skills and fluent English

Nice to Have

• Experience working with vehicle telemetry or automotive datasets

• Infrastructure-as-Code experience (AWS CDK Terraform or similar)

• CI/CD pipeline experience (GitHub Actions GitLab CI or equivalent)

• Experience with event-driven AWS services such as:

  • SQS

  • SNS

  • EventBridge

  • Step Functions

• Experience with Apache Iceberg or Hive

• Previous exposure to multi-terabyte scale data environments

Whats In It For You

• 1200 home office budget

• Flexible allowance of 720/year

• Performance bonus

• Health insurance extendable to your household

• Wellbeing program extendable to your household

• Life insurance

• Pension fund

• Smartphone and phone data plan

• Employee discounts and special financing conditions

• Flexible and remote-friendly work environment

• Flexible working hours

• Multicultural environment with 30 nationalities

• Pet-friendly office culture

• Internal mobility and career growth opportunities

• Collaborative and cross-functional engineering culture

About the Environment

The organization is heavily investing in connected mobility large-scale analytics platforms and digital transformation initiatives that support the future of transportation and operational intelligence.

Engineering teams work on complex distributed systems spanning vehicle software cloud infrastructure analytics platforms fleet management and operational services used globally.

This is an opportunity to work on genuinely large-scale technical problems inside a modern engineering organization where data engineering plays a critical role in shaping future products and business operations.