Enable analytics and reporting by centralizing and integrating high quality, large, complex data sets in a highly performant and scalable cloud analytical platform
Work closely with architects to design data lake architecture, data pipelines, and data publication strategy
Implement data pipelines to ingest, cleanse and enrich data for analytics
Design and implement secure, performant data models to meet the scalability and performance needs of data product
Work with data scientists to enable data discovery and its curation for analytical purposes
Build frameworks, standards & product features to enable self-service analytics
Promote effective team practices, shape team culture, and engage in active mentoring.
Partner with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues
Mentor junior team members
You Have:
10 years of experience with Modern Data Engineering projects and practices: designing, building, and deploying scalable data pipelines
5 + years of experience in designing building deploying cloud native solutions
At least 3 years of experience with AWS, Snowflake, DBT, Python, Docker/Kubernetes, CI/CD, Git, familiarity with: Airflow
Experience in working closely with architects to design and develop data lake, data pipelines, and data product publication strategy. Familiarity with: Data mesh architecture
Developed data products/systems using large and complex data sets to meet business and technical requirements
Worked as a technical lead on the team
Mentored junior team members as needed through design and development
Experience partnering with internal departments to establish requirements
Experience with advanced analytics and machine learning
Experience implementing data management life cycle, using data quality functions like standardization, transformation, rationalization, linking and matching
Familiarity with BI tools such as Tableau, PowerBI
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