Data Analytics Engineer

Analytics & Insights

• Explore time-series HVAC data; produce diagnostics like equipment efficiency, runtime patterns, short-cycling, coil/freezing risk, comfort drift, demand response impact, and fault signatures.
• Define, implement and maintain core KPIs (e.g., kWh/ton, runtime per call, temperature delta vs. setpoint, comfort index, energy per degree-day).
• Build dashboards and reports for product, operations, and customers (e.g., performance baselines, anomaly alerts, weekly summaries).
• Design statistical analyses and A/B-style comparisons (pre/post maintenance, seasonal comparisons, weather-normalized consumption).

 
Data Engineering:

• Design scalable schemas for time-series/telemetry, events, and slowly changing device metadata.
• Build curated feature tables and training datasets for model development (feature engineering, aggregation windows, label generation).
• Implement data quality checks (freshness, validity ranges, unit consistency, missingness, sensor drift detection).
• Collaborate on ingestion/processing pipelines (batch/stream), optimizing cost, latency, and reliability.
• Maintain documentation (data contracts, dictionaries, lineage diagrams).

Collaboration:

• Partner with AI/ML engineers, software developers, and product managers to prioritize analytics that move KPIs.
• Translate stakeholder inputs into well-defined analyses and well-defined metrics for product insertion.
• Required Knowledge, Skills, Abilities, Education and Experience:
• 5 years in data analytics or analytics engineering with time-series or IoT-like data.
• Strong SQL and Python (Pandas/Polars; basic statistical tests, resampling/windowing).
• Hands-on with BI (e.g., Power BI, Tableau, or Looker) and ability to craft clear, story-driven dashboards.
• Experience building clean analytics datasets (e.g., dbt modeling, star schemas, data marts, feature tables).
• Solid understanding of data quality, lineage, and instrumentation for telemetry.
  • Comfortable with at least one modern cloud data warehouse (Snowflake, BigQuery, Redshift, Azure Synapse/Fabric) or lakehouse (Delta/Databricks).