DataOps Engineer

ABOUT GREYSTAR

Greystar is a leading, fully integrated global real estate company offering expertise in property management, investment management, development, and construction services in institutional-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $300 billion of real estate in nearly 250 markets globally with offices throughout North America, Europe, South America, and the Asia-Pacific region. Greystar is the largest operator of apartments in the United States, manages over 1,000,000 units/beds globally, and has a robust institutional investment management platform comprised of nearly $78 billion of assets under management, including over $35 billion of development assets. Greystar was founded by Bob Faith in 1993 to become a provider of world-class service in the rental residential real estate business. To learn more, visit www.greystar.com.

JOB DESCRIPTION SUMMARY

Job Summary: The DataOps Engineer is responsible for managing, monitoring and supporting data pipelines and systems. This role requires deep knowledge of data operations, data engineering, data management and support methodology. Programming knowledge and development activities utilizing PySpark and Object Oriented techniques are a key success factor to this role as is the ability to troubleshoot, prioritize requests, and provide excellent customer support. The DataOps Engineer will monitor, troubleshoot and resolve operational items related to data ingestion and integration. The DataOps Engineer will perform release management activities, will assist with code reviews and process improvement activities and will develop, enhance or refactor code, processes, integrations as required. The ability to create or update documentation is important in this role. The ideal candidate will be a highly skilled collaborator, have a high degree of attention to detail, and have deep knowledge and experience regarding investigation and resolution of process failures or questions from stakeholders. They will likely have deep understanding and experience of data engineering processes and methodologies.

JOB DESCRIPTION

Essential Responsibilities:

• Manage, monitor and support data pipelines written in PySpark, ADF and Boomi to support data integration, transformation, and storage.
• Ensure data quality, consistency, and accuracy within the design and development of the data pipelines.
• Implement and maintain data security solutions on the data models based on the legal, compliance, and business needs.
• Perform deployments and deliver operational support activities related to the development, deployment, operation, and support aspects related to the data pipelines – ensuring accuracy and timely delivery of the data to the business.
• Collaborate with cross-functional teams, including BI/Analytics, data scientists, business analysts, product owners and business stakeholders related to the operational processes – driving customer satisfaction.
• Ensure data quality and integrity through implementation and execution of data validation and monitoring processes. 
• Troubleshoot and resolve data-related issues by serving as the L2 support for tickets.  Collaborate with the Data Engineering team, Product Owners and business stakeholders as needed.
• Document operational processes and keep documentation updated.  Ensure that appropriate documentation exists for pipelines and integrations.
• Generate a variety of operational reports such service ticket metrics, pipeline performance, data quality reports, and so-on.
• Stay current with industry trends and advancements in data engineering and data operations.  Apply best practices to improve data management and operational data processes. Mentor team members to continuously upskill every member of the team. 

Qualifications:

• Bachelor's or advanced degree in Computer Science or Computer Engineering.
• 5+ years of experience in data engineering, with a focus on designing, developing and supporting data pipelines and systems.
• Proficiency in programming and coding with PySpark, Python, and SQL.
• Expertise in Databricks and other databases.
• Strong understanding of data modeling, data quality, and data governance practices.
• Strong understanding of troubleshooting and operational support practices.
• Experience with data integration, ETL processes, and data warehousing.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.

Preferred Qualifications:

• Experience with cloud platforms such as Azure,