Manager, Data Engineering

BarkleyOKRP•Kansas City, MO

About The Position

As the Data Engineer, you will maintain, clean and manipulate data in our operational and analytics databases. You will work with our data scientists, data analytics teams, reporting teams, and IT to understand and aid in the implementation of database requirements, analyze performance, and trouble shoot any existent issues.

Requirements

Bachelor's degree (or equivalent/above) in Computer Science, Information Technology
Very strong on SQL; Must have 3-4+ years of experience working with SQL on large scale datasets
Must have experience in Cloud based Data Engineering with design, implementation and operationalization of large-scale data and analytics solutions, ideally Snowflake
Experience with Python programming language
Must have experience on ETL development life cycle, best practices of ETL pipelines, thorough work experience on data warehouse using combination of Python, BigQuery & GCP Services
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration skills.
Must be able to work under pressure

Nice To Haves

Experience with marketing/advertising huge plus

Responsibilities

Design, develop, and maintain Google BigQuery data warehouse solutions.
Implement data pipelines and ETL processes to support data integration and analytics.
Work with Data Suppliers (technical) for receiving data feeds.
Validate data delivery from Data Suppliers to ensure data is aligned to expectations, resolving technical & data issues as they arise.
Collaborate with cross-functional teams to gather requirements and deliver data solutions.
Maintain operational documents to ensure they are current.
Create optimal data pipeline architecture, troubleshoot and resolve issues related to data integration and job scheduling.
Stay current with trends and best practices in Snowflake and AWS technologies.
Provide support and training to team members on Snowflake best practices.
Identify, design, implement internal process improvements; automate manual processes; improve data delivery, redesign infrastructure for greater scalability.