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Follow →Leads the design, development, and maintenance of data architectures and large-scale processing systems while overseeing a team as a technical lead.
The data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large-scale processing systems. The data scientist, on the other hand, is someone who cleans, massages, and organizes (big) data.
This role would also include the leadership components of a tech lead.
Designs and maintains data pipelines in AWS to feed analytics and ML services, working closely with data scientists and engineers to build scalable ETL workflows using Python, Spark, and SQL.
Builds and maintains web scraping, data pipelines, and real-time alerts for Klar’s Revenue Ops team using Python, Kafka, PostgreSQL, and AWS.
Lead a team to design and build scalable data pipelines on Azure using Databricks, Spark, Python, and Kubernetes that turn retail data into personalized customer insights for Kroger.
Build and scale backend systems for a digital bank in Mexico, using Java 11, Spring Boot, and cloud infrastructure to enable financial access for underserved users.
Lead a team building and scaling Challenger’s data platform, designing pipelines, models, and governance while mentoring engineers and partnering with business stakeholders.