Data Engineer

The Data Engineer role exists to ensure that all test-lab data is trustworthy, accessible, scalable, and usable for engineering, validation, analytics, and decision-making. The function provides the technical backbone that enables reliable testing, advanced analysis, automation, and long-term product improvement.

The Data Engineer is responsible for designing and operating the data infrastructure that supports all laboratory test activities. This includes connecting test equipment, building pipelines for high-frequency and large-volume datasets, ensuring data quality, and enabling engineers, analysts, and scientists to work efficiently with accurate data.

As the laboratory and organization expand, the role will evolve into a leadership function that guides the long-term data strategy and leads a multidisciplinary data team.

Key Responsibilities

Key responsibilities include (but are not limited to)

• 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 practices

Requirements

Qualifications and Experience

• Engineering 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

• Experience in large manufacturing or industrial enterprises with heterogeneous, distributed data sources, demonstrating ability to navigate complexity at scale

Specific skills & Knowledge

• 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