Data Engineer Senior

Team & Position Summary

Acuity Brands is seeking a Senior Data Engineer to join its Atrius Analytics team. Acuity Brands is driving smarter, safer, and greener outcomes across industries like building automation, HVAC, AV systems, refrigeration, and lighting. By harnessing valuable data within a space, we are powering advanced analytics and AI-driven insights that transform environments and maximize occupant experiences. 

The Atrius Analytics team is building a scalable, intelligent data platform that ingests and normalizes IoT telemetry from across our diverse verticals. Supporting both batch and real-time processing, this platform provides the foundation for industry-leading cloud applications in building performance and spatial intelligence. We value trust, respect, asynchronous communication, creativity, and customer focus. Our day-to-day is guided by an agile design methodology, with close collaboration among software engineers, firmware engineers, data scientists, and third-party developers. Our team works across Azure, AWS, and GCP IOT services.

The ideal candidate is passionate about driving impact through data while developing business, technical, and leadership acumen.  

Primary Responsibilities Include

As a Data Engineer, you will play a critical role in architecting and developing pipelines that transform raw IoT data into actionable intelligence. You will build robust data models that ensure data quality and consistency, enabling downstream analytics and AI/ML applications that optimize operations, enhance user experiences, and unlock new business opportunities. Some responsibilities include:

• Design and implement scalable data engineering pipelines for ingesting, transforming, and storing IoT telemetry data using Apache Flink, Apache Spark, and Databricks
• Build and maintain time-series data solutions using PostgreSQL and TimescaleDB to support high-resolution telemetry analytics
• Integrate data governance frameworks for metadata management, lineage tracking, and compliance within a data lake ecosystem
• Apply performance tuning techniques in Databricks (e.g., partitioning, caching, adaptive query execution) to optimize batch processing speed and resource utilization.
• Design and implement Infrastructure as Code (IaC) solutions using Terraform and Azure Bicep to provision and manage cloud resources across multi-cloud environments
• Optimize and monitor pipeline performance and resource utilization across distributed environments such as Kubernetes and Databricks clusters
• Leverage telemetry and diverse data sources to design, test, and deploy AI/ML solutions, collaborating with data scientists and engineers to build deep learning capabilities within the platform for prediction, early alerting, and prescriptive recommendations
• Define appropriate business metrics to measure the impact of AI/ML solutions within the platform
• Write scalable, distributed, and highly efficient code, in languages such as Python, Java, PySpark, Scala, and R

Qualifications

• A BS in Computer Science, Statistics, Mathematics, or a related field
• 5+ years of experience in building end-to-end analytics solutions, including designing and implementing real-time and batch data pipelines for IoT telemetry or time-series data 
• Excellent problem-solving, critical thinking, and communication skills 
• Demonstrated initiative to find solutions to complex problems at scale and operationalize them 
• Demonstrated ability to work in ambiguous situations and across organizational boundaries
• Lead with respect, accountability, integrity, and a positive can-do attitude  
• Experience working in a data-intensive environment and translating business needs into data requirements 
• 2+ years of experience in building and operationalizing analytics pipelines and services, adopting the container ecosystem of Docker and Kubernetes 
• 2+ years of demonstrated AI/ML pipeline development with relevant code experience 
• 2+ years’ experience using one or more of the following: TensorFlow, MLFlow, PyTorch, SparkML, etc. 
• 2+ years’ experience using one or more of the following frameworks: Apache Flink, Spark, Spark (structured) Streaming, Akka, Kafka, etc. 
• 2+ years’ experience  building scalable batch data pipelines on cloud-based platforms, such as Databricks