> Markdown version of [/jobs/ext/2156451-data-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/2156451-data-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analytics Engineer - **Company:** After School Matters, Inc. - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Systems Engineering, Confluence, Microsoft Azure, BigQuery, Cloud Computing Security, Databases, Data Validation, Information Engineering, Data Mart, Data Warehousing, Statistical Hypothesis Testing, Identity and Access Management, Python (Programming Language), OAuth, Role-Based Access Control, Power BI, Azure Active Directory, Prometheus, Kusto Query Language, Standard Sql, Azure Data Lake, SQL Databases, Data Streaming, Tableau (Software), Management of Software Versions, Azure Service Bus, Feature Engineering, Azure Data Factory, Snowflake, Apache Spark, Deep Learning, Git, Pandas, Low Latency, Influxdb, Apache Flink, Data Analytics, Apache Kafka, Spark Streaming, Machine Learning Operations, Vertica, Api Design, Stream Processing, Azure Synapse Analytics, Stream Analytics, Looker Analytics, Data Pipelines, Key Vault, Amazon Redshift, Databricks - **Published:** August 20, 2026 - **Apply:** https://aiirproducts.applytojob.com/apply/jN3pnrDstT/Data-Analytics-Engineer?source=GS ## About the Role 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., * 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)., * 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. ## Description * 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). ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Keeping applications secure by evolving OAuth 2.0 and OpenID Connect](https://www.wearedevelopers.com/videos/100152-keeping-applications-secure-by-evolving-oauth-2-0-and-openid-connect) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)