PowerLabs is an energy and climate tech research and deployment company on a mission to create a planet with limitless human productivity through intelligent energy. With an aging grid, rising power intermittency and energy costs, individuals, businesses and communities need energy intelligence at their fingertips.
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As a Data Engineer, you will design, implement, and maintain data ingestion, storage, optimisation, processing (pre/post), and analytics pipelines for time-series IoT and energy data.
You will work closely with data scientists/analysts, software engineers, and optimisation experts to ensure seamless real-time and batch data workflow integration.
Required Skills & Qualifications
3+ years of experience in data engineering with a focus on IoT, energy, or industrial data.
Proficiency in Python and SQL; experience with Scala is a plus.
Experience building scalable ETL/ELT pipelines using Apache Airflow, NiFi, Prefect etc.
Strong knowledge of distributed data processing frameworks (Dask, Spark, Flink, or Kafka Streams).
Hands-on experience with both relational and NoSQL databases, including PostgreSQL, MySQL, Cassandra, DynamoDB, or MongoDB.
Hands-on experience with time-series databases (TimescaleDB, InfluxDB, ClickHouse, or DuckDB).
Familiarity with data lake architectures and formats (Apache Iceberg, Delta Lake, Parquet).
Experience with cloud data services (AWS: S3, Glue, Lambda, Athena; GCP: BigQuery, Dataflow).
Ability to optimise SQL queries and database performance.
Experience in MLOps and data pipelines for ML workflows.
Knowledge of energy systems, SCADA protocols, or grid optimisation is a plus.
Strong problem-solving skills and ability to work in cross-functional teams.
Important
This role is strictly not for vibe-coders.
Do not apply if you rely more on guesswork than on clear data, prefer quick fixes over solid design, or value style over reliability.
We need engineers who work carefully, follow strong processes, and deliver real, measurable results; anything less will not meet our standards.
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