Data and ML Engineer (Remote)

Solstice Analytics is partnering with a fast-growing energy technology company to find a Data and ML Engineer to join their team. This is a remote position, with a start date as soon as the candidate successfully completes the interview process. Monthly B2B salary: 21000 - 27000 PLN net. About the Role: As a Data and ML Engineer, you will take ownership of our client's data pipelines and bring advanced analytics and optimization models into production. The role sits at the intersection of data engineering, applied machine learning, and energy systems, bridging the data science and software engineering efforts. Our client is building the energy company of the future: installing large-scale batteries at industrial sites and connecting them into an intelligent network that manages electricity in real time. This turns factories into energy hubs and makes intermittent renewables reliable, dispatchable, low-cost power around the clock. You will join an early-stage environment where the data platform is being built from scratch, scoping pragmatically and making opinionated decisions without waiting for perfect requirements. Key Responsibilities: Take end-to-end ownership of the data pipelines: ingest high-volume time-series feeds (telemetry, forecasts, energy market data) and make them clean, validated, and reliably available to everything downstream Build data quality in by default, with validation, anomaly detection, and monitoring wired into every pipeline rather than bolted on afterwards Shape the data platform architecture as it grows, balancing real-time responsiveness with heavy historical processing Partner with data scientists to take forecasting and optimization work from notebook to production, and stay on the hook for how those systems behave once they are live Push the quality of the modeling and algorithm work itself through code review, technical debate, and hands-on contribution, not just plumbing around it Embed data and models directly into the core product alongside the engineering team Own the data relationship with external partners (EMS providers, trading partners) so integrations stay clean at the source Candidate Profile: 3-6 years in data or ML engineering, or applied data science, with a clear track record of shipping to production and living with what you shipped Python fluency where clean, tested, production-grade code is your baseline, not your best day Hands-on with ETL/ELT pipelines, data modeling, and the specific quirks of time-series data Strong PostgreSQL, including schema design and query tuning under real load Treat data quality, validation, and monitoring as first-class engineering, not an afterthought Comfortable working close to the metal on AWS, without an abstraction layer doing the thinking for you Have run models and pipelines in production before, whether as APIs, batch jobs, or scheduled workflows Thrive in an early-stage setting: you scope pragmatically, make a call with imperfect information, and keep moving Nice to have: Exposure to energy markets, industrial systems, or hardware-adjacent data such as sensor streams and EMS integrations Interview Process: Phone screening Technical interview Culture fit interview If you are a passionate Data and ML Engineer eager to help build the energy company of the future and grow within a dynamic team, we would love to hear from you.