Job Description
Our client operates at the crossroads of energy and home services, fueled by the vision of a smarter, cleaner future. Committed to developing groundbreaking solutions, they aim to streamline their customers’ lives by providing energy, protection, and smart services for their homes and businesses.Our client is seeking a Senior Data Engineer to build and scale the pipelines and data products that transform raw device telemetry into reliable, actionable intelligence.
- Data Pipeline Development: Design and maintain scalable ETL/ELT pipelines that process time-series signals from thermostats, weather, schedules, and device analytics.
- Core Data Products: Build verified HVAC and Energy data tables (e.g., Run & Drift, Thermal Coefficients, Efficiency Drift) to serve as trusted sources for analytics, modeling, and automation.
- Modernization & Quality: Refactor legacy Scala/Akka processes into PySpark or Databricks jobs, improving observability, testing, and CI/CD coverage for upstream feeds.
- Integration & Streaming: Manage data sourced from Mongo-based telemetry, Kafka or Pub/Sub streams, and cloud storage (GCS) to ensure reliability and consistency.
- Model Enablement: Collaborate with data scientists to generate and operationalize features supporting HVAC runtime prediction, anomaly detection, and DR optimization.
- Documentation & Governance: Promote best practices for data lineage, schema documentation, and change control to prevent regressions in production systems.
Required Qualifications
- 3+ years of data engineering or backend data systems experience
- Strong proficiency in Python, SQL, and distributed data frameworks (PySpark, Databricks)
- Hands-on experience with GCP, Kafka/Pub-Sub, and data lake architecture
- Ability to read and modernize legacy Scala/Akka codebases
- Proven track record building production-grade pipelines that deliver analytics-ready datasets
- Strong problem-solving skills in ambiguous, under-documented environments
Preferred Qualifications
- Experience with ML platforms and feature engineering workflows (e.g. Vertex AI)
- AI/ML application experience (LLMs, computer vision, energy forecasting models)
- Background in IoT applications, protocols, and telemetry
- Familiarity with specialized databases:
- Graph databases (e.g. Neo4j)
- Vector databases (e.g. Pinecone)
- Experience with data orchestration tools (e.g. Airflow)
- Background in Demand Response or home energy automation
- Experience implementing data quality metrics, observability, and alerting
- Track record of significant cost optimization or performance improvements in data systems