Data Engineer — IoT, Telemetry and Technical Pipelines

About the role

This person will work on the integration, quality and processing of WDT technical data. The work includes fusion of sensors, telemetry, geospatial data, inspections, documents, historical series, InSAR and field evidence, and preparation of traceable, reliable data for RAG, Graph-RAG and evaluations.

The role combines data engineering with an understanding of technical context. The aim is for field data, assets, sensors, inspections and external sources to reach the WDT in an organized, verifiable form that is useful for analytics, AI and engineering decisions.

Main responsibilities

• Create pipelines to ingest, transform, validate and publish technical data for the WDT.
• Integrate sensors, gateways, edge, telemetry, time series, operational data and asset events.
• Structure data from inspections, documents, history, evidence, geodata and analytical results.
• Define standards for quality, traceability, provenance, versioning and handling of inconsistencies.
• Support analytics, risk engines, executive panels, AI pipelines and preparation of technical corpora.
• Collaborate with backend, AI, DevOps, QA, 3D/GIS modeling and domain engineering.
• Document data flows, validation criteria and known limitations.

Required qualifications

• Experience with Python, SQL and building data pipelines.
• Knowledge of ETL/ELT, APIs, integration of heterogeneous sources and data quality.
• Familiarity with relational databases, NoSQL, file storage and semi-structured data.
• Ability to work with time series, events, logs or operational data.
• Attention to traceability, versioning, data origin and technical documentation.
• Interest in engineering data, sensors, inspections, assets or digital twins.

Preferred qualifications

• Experience with IoT, telemetry, edge, gateways, InSAR, GIS or geospatial data.
• Knowledge of vector databases, corpus preparation for RAG, OCR or technical parsing.
• Background in Azure, cloud, observability, automated pipelines and segregated environments.
• Familiarity with risk models, dashboards, analytics or applied machine learning.

Related technologies and tools

Python, SQL, APIs, ETL/ELT, NoSQL, time series, IoT/telemetry, cloud, with vector databases as a plus.

Profile

We are looking for a methodical, investigative person who values reliable data as much as working code. They should be able to talk with software, engineering and field operations and turn real data into useful inputs.

How to apply

To apply, send the following materials:

How do you currently use artificial-intelligence tools in your professional workflow? Name the tools, give examples of activities, and explain how you validate the results produced with AI assistance.

Additional information may include LinkedIn, GitHub, Behance, Google Scholar, articles, publications, websites, demonstrations, applications or other links relevant to the role.

• An up-to-date curriculum vitae with education, professional experience, main projects, technologies used and responsibilities held.
• A portfolio with concrete examples of previous work. For software, this may be GitHub, applications, systems, APIs, interfaces or projects that can be demonstrated. For engineering, this may be models, simulations, reports, analyses, publications or technical work that can be shared. Confidential projects may be presented in anonymized form.
• A presentation video of up to 5 minutes, briefly showing a project or portfolio, the problem solved, your contribution, the technologies used, your working workflow, everyday tools, your use of artificial intelligence and how you validate results produced with AI support.