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.
Role Overview:
- As a Data Engineer, you will design, implement, and maintain data ingestion, storage, optimization processing (pre/post), and analytics pipelines for time-series IoT and energy data. You will work closely with data scientists/analysts, software engineers, and optimization experts to ensure seamless integration of real-time and batch data workflows.
Key Responsibilities:
- Design and implement ETL/ELT pipelines for ingesting data from IoT sensors, third-party APIs, and industrial protocols (MODBUS, OPC-UA, MQTT).
- Develop real-time and batch data processing systems using Kafka, RabbitMQ, Apache Flink, and Spark/PySpark.
- Optimize storage and retrieval of high-frequency time-series data using TimescaleDB, InfluxDB, or DuckDB.
- Design and maintain data lake and warehouse solutions (S3 + Apache Iceberg, Delta Lake, or BigQuery).
- Build and maintain pre-processing and post-processing pipelines for optimization workflows, ensuring seamless integration with forecasting and dispatch models.
- Design, develop, and maintain robust APIs to expose processed data and insights for applications, dashboards, and external integrations.
- Work closely with backend and DevOps teams to ensure seamless API deployment, scalability, and security.
- Work with cloud/DevOps engineers to implement monitoring and alerting systems to ensure high availability and fault tolerance.
- Work with machine learning and optimization teams to provide clean and structured datasets for forecasting and energy dispatch models.
- Ensure data security and compliance with industry standards.
- Automate and orchestrate workflows using Apache Airflow, Terraform, Lambda etc.
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 optimize SQL queries and database performance.
- Experience in MLOps and data pipelines for ML workflows.
- Knowledge of energy systems, SCADA protocols, or grid optimization is a plus.
- Strong problem-solving skills and ability to work in cross-functional teams.
Nice-to-Have:
- Experience with real-time stream processing (Flink, Kafka Streams).
- Familiarity with Pyomo, Gurobi, or other optimization frameworks for LPs and MIPs.
- Previous experience in industrial automation or energy sector projects.
Method of Application
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