Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

Tools in this posting

Python
SQL
Azure
BigQuery
ClickHouse
Databricks
dbt
Delta
Kafka
Looker
Prefect
Redshift
Snowflake
Spark
Tableau
Airflow
Dagster
Power BI
pandas
Polars

Source

- 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.

Preferred Qualifications:- Experience working with Kamea or similar API‑first IoT device management platforms (device registry, telemetry, events). - Comfortable consuming platform REST APIs (Python/SQL pipelines) and handling OAuth2/SSO with role/permission scopes. - Working knowledge of cloud security fundamentals: IAM, RBAC, managed identities, network boundies, Key Vault (secrets / keys / certs), and encryption,

- Alternatives: Kafka, Snowflake, BigQuery, Redshift; InfluxDB/TimescaleDB for specialized time-series use cases. - Languages: SQL, Python. Versioning: Git. Documentation: dbt docs/Confluence. Observability: Great Expectations/Monte Carlo (or equivalent DQ tooling). Ready for Your Next Big Adventure?

- 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). Preferred Qualifications:- Experience working with Kamea or similar API‑first IoT device management platforms (device registry, telemetry, events).

- HVAC or building systems domain exposure (BMS/BAS, AHUs, VAVs, heat pumps, demand response, maintenance logs). - Time-series databases or engines: Azure Data Explorer (ADX/Kusto), InfluxDB, TimescaleDB, Prometheus, ClickHouse and Spark. - Streaming/ingestion: Kafka, Azure Event Hubs/IoT Hub, Kinesis, Pub/Sub; stream processing (Flink, Spark Structured Streaming, Azure Stream Analytics).

- Time-series databases or engines: Azure Data Explorer (ADX/Kusto), InfluxDB, TimescaleDB, Prometheus, ClickHouse and Spark. - Streaming/ingestion: Kafka, Azure Event Hubs/IoT Hub, Kinesis, Pub/Sub; stream processing (Flink, Spark Structured Streaming, Azure Stream Analytics). - Modeling support: feature stores (Feast/Databricks), ML-ready dataset design, basic MLOps familiarity.

- Modeling support: feature stores (Feast/Databricks), ML-ready dataset design, basic MLOps familiarity. - Data orchestration: Airflow, Azure Data Factory, Prefect, Dagster; dbt for transformation. - Geospatial and weather normalization experience (e.g., degree-days, ASHRAE concepts).

- Tooling (Example Stack) - Azure-first: Azure AD/Entra ID/IoT Hub/Event Hubs → ADLS Gen2 → Databricks (Delta) / Synapse / Fabric → Power BI; ADX for large time-series exploration and fast ad-hoc queries. - Alternatives: Kafka, Snowflake, BigQuery, Redshift; InfluxDB/TimescaleDB for specialized time-series use cases.

- Azure-first: Azure AD/Entra ID/IoT Hub/Event Hubs → ADLS Gen2 → Databricks (Delta) / Synapse / Fabric → Power BI; ADX for large time-series exploration and fast ad-hoc queries. - Alternatives: Kafka, Snowflake, BigQuery, Redshift; InfluxDB/TimescaleDB for specialized time-series use cases. - Languages: SQL, Python. Versioning: Git. Documentation: dbt docs/Confluence. Observability: Great Expectations/Monte Carlo (or equivalent DQ tooling).

- Streaming/ingestion: Kafka, Azure Event Hubs/IoT Hub, Kinesis, Pub/Sub; stream processing (Flink, Spark Structured Streaming, Azure Stream Analytics). - Modeling support: feature stores (Feast/Databricks), ML-ready dataset design, basic MLOps familiarity. - Data orchestration: Airflow, Azure Data Factory, Prefect, Dagster; dbt for transformation.

- 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.

- 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).

Job description

View original posting ↗

Job Title: Data Analytics Engineer
Location: Bangalore, KA
Engagement: Fulltime

About AIIR Products:
AIIR focuses exclusively on delivering innovative, sustainable systems that leverage advanced machine learning technology. Our flagship product, the AIIR Intelligent HVAC system, delivers greater energy efficiency compared to traditional systems, intelligently adapting to environmental conditions and occupant needs in real time.

AIIR is committed to creating healthier, more comfortable living spaces through smart technology. Its steadfast dedication to sustainability and innovation drives the development of solutions that improve project outcomes and positively impact the planet.

Data Analytics Engineer
AIIR has an exciting and important opportunity for the right person to join our Intelligent HVAC Systems engineering and product development team to design and commercialize our own unique, purpose-built HVAC systems as a Data Analytics Engineer. This is a hands-on and highly visible opportunity in a rapidly growing and evolving company with lots of room to grow! The Data Analytics Engineer will bring data analytics and brings solid data engineering fundamentals to work with high-volume HVAC telemetry (e.g., temperatures, humidity, power, compressor/fan states, setpoints, faults, weather) to deliver diagnostics, customer-facing insights, and business-critical KPIs—and you’ll build the clean, reliable data assets that power our model development and product analytics. We are seeking a person with at least 5 years demonstrated experience with data analytics and data engineering related to complex engineering systems.

Major Areas of Responsibility:

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).

Preferred Qualifications:

Experience working with Kamea or similar API‑first IoT device management platforms (device registry, telemetry, events).
Comfortable consuming platform REST APIs (Python/SQL pipelines) and handling OAuth2/SSO with role/permission scopes.
Working knowledge of cloud security fundamentals: IAM, RBAC, managed identities, network boundies, Key Vault (secrets / keys / certs), and encryption,
HVAC or building systems domain exposure (BMS/BAS, AHUs, VAVs, heat pumps, demand response, maintenance logs).
Time-series databases or engines: Azure Data Explorer (ADX/Kusto), InfluxDB, TimescaleDB, Prometheus, ClickHouse and Spark.
Streaming/ingestion: Kafka, Azure Event Hubs/IoT Hub, Kinesis, Pub/Sub; stream processing (Flink, Spark Structured Streaming, Azure Stream Analytics).
Modeling support: feature stores (Feast/Databricks), ML-ready dataset design, basic MLOps familiarity.
Data orchestration: Airflow, Azure Data Factory, Prefect, Dagster; dbt for transformation.
Geospatial and weather normalization experience (e.g., degree-days, ASHRAE concepts).
Statistical techniques: anomaly detection heuristics, confidence intervals, hypothesis testing, change-point detection.
Tooling (Example Stack)
Azure-first: Azure AD/Entra ID/IoT Hub/Event Hubs → ADLS Gen2 → Databricks (Delta) / Synapse / Fabric → Power BI; ADX for large time-series exploration and fast ad-hoc queries.
Alternatives: Kafka, Snowflake, BigQuery, Redshift; InfluxDB/TimescaleDB for specialized time-series use cases.
Languages: SQL, Python. Versioning: Git. Documentation: dbt docs/Confluence. Observability: Great Expectations/Monte Carlo (or equivalent DQ tooling).

Ready for Your Next Big Adventure?

If you are a strategic thinker and innovation in the HVAC industry, we want to hear from you! Please submit your resume and a cover letter detailing your relevant experience.

AIIR Products is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.