KODE Labs
Data Engineer - Energy Data
Builds production-grade data pipelines to validate, clean, and normalize energy meter data into trusted datasets, collaborating with engineering and domain teams to ensure scalability and accuracy.
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Strong data engineering experience working with IoT, meter, operational, or high-volume time-series data
- Strong SQL and Python skills, with software engineering experience to build reliable production systems
- Experience building data validation, cleaning, anomaly detection, and data-quality pipelines
- Strong understanding of time-series data challenges, including gaps, irregular intervals, duplicates, timestamps, stale data, and outliers
- Understanding of energy concepts such as consumption, demand, cumulative and interval meters, cost, EUI, and unit normalization
- Experience building scalable data pipelines and working with modern data processing and storage technologies
- Strong problem-solving skills and ability to translate complex data-quality scenarios into reusable engineering solutions
- Ability to collaborate effectively across data, software engineering, product, and domain teams
Full description
Our team at KODE Labs is looking for a Data Engineer — Energy Data to build the systems that turn raw energy and meter data into trusted, calculation-ready datasets.
In this role, you’ll combine strong data and software engineering skills with an understanding of time-series and energy data. You’ll define validation and cleaning logic, build production-grade pipelines, and partner with software engineers to turn these capabilities into scalable, reusable parts of KODE OS.
What You Will Do
- Define and implement energy meter validation rules across integrations, meter types, units, reporting frequencies, and building configurations.
- Build production-grade validation, cleaning, and normalization pipelines that transform raw meter data into trusted, calculation-ready datasets.
- Validate incoming integration data for completeness, freshness, schema consistency, timestamps, units, and expected behavior.
- Use Digital Twin context to validate meter identity, units, hierarchy, equipment relationships, and expected reporting frequency.
- Develop detection and handling logic for spikes, outliers, gaps, duplicates, stale values, and missing data.
- Build robust logic for cumulative-to-interval conversion, including meter resets, rollovers, replacements, negative deltas, and invalid drops.
- Establish quality flags, confidence indicators, and clear lineage between raw, cleaned, and corrected values.
- Develop reusable validation patterns that can scale across customers, buildings, integrations, and different types of meters.
- Build monitoring and observability around energy data quality so issues can be identified before they impact downstream calculations or customer-facing products.
- Partner with Energy, Integrations, Digital Twin, and Data teams to understand upstream data behavior and downstream calculation requirements.
- Work closely with software engineers to productize validation logic into reusable tooling, APIs, monitoring workflows, and automated validation systems.
- Continuously improve validation and cleaning approaches as new integrations, meter types, and data-quality scenarios emerge.
Requirements
Requirements
- Strong data engineering experience working with IoT, meter, operational, or high-volume time-series data.
- Strong SQL and Python skills, with the software engineering experience to build reliable production systems.