Photonic R&D Data Engineer: Pipelines & Lineage

Ligentec; Ecublens; In loco; CHF 120.000 - 170.000; Tempo pieno

Vantaggi offerti da questo lavoro

Hybrid work

Home office: up to 2 days per week

Descrizione del lavoro

Ligentec in Lausanne seeks a Research Data Engineer to build the analysis library used by our engineering teams to turn test data into decisions, and to connect data flows from layout to measurement.

You will own the infrastructure for data movement, ensure traceability across teams, and develop scalable Python libraries for characterization data, ETL pipelines, and metadata handling.

Competenze

• MSc in CS, Eng, or related field.
• 5+ years in data in an R&D or instrument-heavy environment.
• Strong Python with library and API design.
• SQL, Git, and UNIX/bash proficiency.
• Experience ETL/ELT pipelines with Prefect, Airflow, Dagster.
• Familiarity with HDF5, Zarr, Parquet formats.
• Proficient English, able to coordinate across teams.
• Willing to adopt new frameworks and tools.

Mansioni

• Architect a Python library for characterization data orchestration.
• Abstract analysis logic from storage schemas.
• Scale solutions for local, remote, and distributed execution.
• Add logging and dataframe lineage with DB export.
• Support exploratory R&D and production QA workflows.
• Document and assist adoption across teams.
• Build multi-site data storage and metadata storage.
• Create ingestion for relational metadata updates.
• Enable cross-team automation via metadata handshakes.
• Standardize data retrieval and storage for traceability.

Conoscenze

Python

Data analysis

English communication

Formazione

MSc in Computer Science or related field

Strumenti

SQL

Git

UNIX/bash

Prefect

Airflow

Dagster

HDF5

Zarr

Parquet

Ray

Dask

Spark

Descrizione del lavoro

Ligentec in Lausanne seeks a Research Data Engineer to build the analysis library used by our engineering teams to turn test data into decisions, and to connect data flows from layout to measurement.

You will own the infrastructure for data movement, ensure traceability across teams, and develop scalable Python libraries for characterization data, ETL pipelines, and metadata handling.