Data Engineer

Atlanta GA   Computer Software Posted: 2 weeks ago  

Job Description:
Our client is looking to roll out new features and offerings to their existing IoT building management products servicing commercial customers worldwide. This role will play a crucial part empowering customers to save energy and optimize building performance by creating new data pipelines, integrations, models, and machine learning workflows – directly impacting the quality and scalability of analytics. Huge opportunity to join a team with a mission to make waves in the smart/sustainable buildings industry!

Requirements:
• 7+ years’ experience in data engineering – managing data systems for analytics applications (BI, reporting, dashboards)
o Including data modeling, warehousing, and ETL
• 3+ years’ experience in the manufacturing, factory, or building equipment industry (taking data from physical equipment)
• Extensive experience with AWS services – RedShift, S3, and Glue
• Experience with both JavaScript and Python
• Great communication skills – ability to collaborate with business leaders, gather customer requirements, and technical teams to provide data solutions
• B.S. degree in Computer Science, Data Management, Statistics, or related field

Nice to have:
• AWS Big Data certifications
• Experience with building automation or building management systems (BMS)

Responsibilities:
• Design and develop scalable and reliable data architecture for analytics products, ensuring optimal performance and alignment with business objectives.
• Gather business and functional requirements from product managers, analytics designers, and customer and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture. Develop and maintain data models and schemas that support complex data analysis and predictive modeling.
• Designing and implementing data integration solutions to facilitate the seamless flow of data between different systems, applications, and databases. This may involve using Extract, Transform, Load (ETL) tools, data replication techniques, or application programming interfaces (APIs) to integrate disparate data sources.
• Establishing data governance policies, standards, and procedures to ensure the integrity, security, and quality of the organization’s data assets. This involves defining data management practices, enforcing data compliance requirements, and implementing data quality controls. Establishing data quality standards and processes to monitor, measure, and improve the quality of the organization’s data assets. This involves identifying data quality issues, implementing data cleansing and enrichment techniques, and establishing data quality metrics and KPIs.
• Developing and maintaining metadata repositories to catalog and organize metadata about the organization’s data assets. This includes capturing metadata attributes such as data lineage, data definitions, and data ownership to facilitate data discovery and understanding

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