Data Engineer

Lyten Labs AB; Västerås kommun; On-site; SEK 550,000 - 700,000; Full time

Job summary

Lyten Labs AB in Västerås is looking for a Data Engineer specializing in Test & Validation. The role involves designing and maintaining data pipelines for high-frequency lab data, integrating various test equipment, and developing ETL/ELT processes.

The ideal candidate should have over 4 years of experience in a technical data role and strong programming skills in Python and SQL. Familiarity with both AWS and GCP platforms is essential, along with experience in building reliable data architectures.

Qualifications

• 4+ years of relevant experience in Data Engineering, Data Science, or Machine Learning.
• Hands-on experience with scaling data platforms and cloud environments.
• Familiarity with time-series and high-frequency measurement data.

Responsibilities

• Design, build, and maintain data pipelines for high-frequency lab data.
• Automate data processing for reporting, dashboards, and analysis.
• Ensure data traceability, version control, and audit compliance.

Skills

Data Engineering

Python

SQL

ETL/ELT frameworks

Cloud engineering

Data validation

Education

Engineering background

Tools

AWS

GCP

Docker

Job description

Position: Data Engineer – Test & Validation

Responsibilities

• Design, build, and maintain data pipelines for high‑frequency lab data
• Integrate test equipment such as battery cyclers (Chroma, Keysight, PEC, PNE), chambers, DAQ systems, and PLCs
• Develop ETL/ELT processes to transform raw → validated → curated datasets
• Build scalable data storage solutions (data lakes, time‑series DBs, structured metadata stores)
• Implement data validation, anomaly detection, and quality monitoring
• Automate data processing for reporting, dashboards, and analysis
• Ensure data traceability, version control, and audit compliance
• Work closely with test and validation engineers to understand test profiles, metadata, and measurement methods
• Support lab technicians with tools that simplify workflows and reduce manual data tasks
• Integrate with MES, LIMS, PLM, and other enterprise systems
• Troubleshoot data‑related issues in test execution or equipment communication
• Take increasing ownership of data architecture and long‑term data roadmap
• Contribute to documentation standards, data governance, and best‑practice development

Qualifications

• Engineering background in technical data role (Data Engineering, Data Science, Machine Learning) including processing, storage, quality, and management on GCP or AWS – 4+ years of relevant experience
• Project management experience is beneficial
• Experience in large manufacturing or industrial enterprises with heterogeneous, distributed data sources, demonstrating ability to navigate complexity at scale
• Proven experience scaling and re‑architecting data platforms and infrastructure to handle rapid growth and increasing data volumes
• Hands‑on experience designing and building highly scalable and reliable data architectures using modern cloud and data tooling (e.g., AWS Kinesis, Lambda, Redshift, GCP equivalents; Airflow, dbt; Parquet, Protobuf, Avro)
• Strong programming skills in Python, SQL, and general‑purpose scripting for automation, data processing, and integration
• Deep understanding of ETL/ELT frameworks (Airflow, dbt, Spark, etc.) and experience building production‑grade data pipelines
• Familiarity with time‑series and high‑frequency measurement data, particularly from industrial or test environments
• Cloud engineering experience in AWS, GCP, or Azure, including serverless architectures, distributed storage, and stream processing
• Experience with CI/CD, Git‑based workflows, Docker, and robust software engineering practices
• Knowledge of data serialization formats (Parquet, Avro, Protobuf, JSON) and best practices for efficient storage and retrieval
• Experience integrating systems via APIs; familiarity with hardware communication protocols such as REST, OPC‑UA, and Modbus is a strong plus
• Understanding of machine learning concepts and experience supporting data scientists with structured, high‑quality datasets
• Domain knowledge (Preferred): Solid engineering foundation (electrical, mechanical, chemical, physical), preferably within the energy, electrical testing, or battery domain
• Understanding of sensor calibration, noise, drift, and data validation