Recruiter
Alexandra Frolkina
Roles:
Data
Must-have skills:
DatabricksDockerPython
Nice-to-have skills:
Kubernetes
Considering candidates from:
Europe
Europe
Work arrangement: Remote
Industry: Information Services
Language: English
Level: Senior
Required experience: 5+ years
Size: 2 - 10 employees
Company
Renewcast is a leader in wind power generation forecasting, offering a proprietary SaaS platform that provides more accurate day-ahead and intra day wind production forecasts based on weather and wind turbine generator data. This technology delivers significant cost savings for wind park operators.
Description
Now the company is looking for a Data Engineer to join their growing Data & ML platform team
Tasks:
Tasks:
- Own and maintain all Databricks-based data pipelines, from ingestion to transformation to delivery
- Design and optimize workflows for performance, clarity, and cost —including compute strategy, parallelization, and dependencies
- Help evolve the orchestration layer (e.g., transition workflows to Prefect, Dagster, or similar frameworks running on Kubernetes)
- Contribute to CI/CD processes: build and test pipelines, manage Docker-based execution environments, and support multi-stage deployment flows
- Develop and maintain Docker images used for Databricks jobs, with attention to reproducibility and efficiency
- Translate exploratory code from meteorologists and scientists into well-structured, production-ready pipelines
- Monitor and improve data pipeline performance, cost, and stability
- Coordinate with the MLOps and DevOps engineers on shared infrastructure, compute setup, and deployment mechanics
Must-have:
- 5+ years of experience in data engineering or data infrastructure roles
- Solid hands-on experience with Azure Databricks, Delta Lake, and Python-based data tooling
- Strong knowledge of Docker, especially in the context of CI/CD and runtime environments
- Experience with data-focused CI/CD pipelines (e.g., GitHub Actions or similar), including testing, promotion, and reproducibility
- Familiarity with modern workflow orchestrators (e.g., Prefect, Dagster, Airflow) and DAG-based execution models
- Solid understanding of staging and production environments, and how to ship safe and testable changes across them
- Proven ability to diagnose and resolve complex issues in distributed data systems
- Clear grasp of the full lifecycle of a pipeline: testing, validation, staging, deployment, and monitoring
- Fluent in English, with the ability to travel for team meetings across Europe
- Must be eligible to live and work in the EU without sponsorship
Nice-to-have:
- Experience working with large-scale tensorial or gridded datasets (e.g., ZARR, GRIB2, NetCDF, or similar)
- Understanding of geospatial and temporal data patterns, especially in forecasting or climate modeling
- Prior exposure to ML infrastructure, particularly feature extraction, batch inference, or model-serving pipelines
- Familiarity with Kubernetes for running data workflows or jobs at scale
- Comfort managing data cost-performance tradeoffs, e.g., compute provisioning, Spark tuning, or caching strategies
- Hands-on experience integrating custom Docker containers into orchestration environments (e.g. via Databricks, Kubernetes, or custom schedulers)
- Understanding of how to work alongside product and research teams to turn ad-hoc code into reproducible, maintainable components
Benefits and conditions:
- Trial period: 3 months
- A dynamic start-up environment where you’ll have the freedom to shape your role
- A young, close-knit, and ambitious international team of data specialists
- Competitive compensation package plus Equity Stock Option Plan (ESOP)
- Ability to make an impact in the clean-tech space
- Flexibility of remote work with occasional collective meetups across Europe
Interview process:
- Intro call with Toughbyte
- 30-minute call with ML and Data Platform Manager
- Call with CTO
- Case study (1,5 hours long) + case evaluation (1 hour) - optional
- Call with SEO
Questions
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B2B can be an option