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","responsibilities":"Partner with Data Science, Product Manager, Analytics, and Business teams to review and gather the data/reporting/analytics requirements and build trusted and scalable data models, data extraction processes, and data applications to help answer complex questions.
Design and implement data pipelines to ETL data from multiple sources into a central data warehouse.
Design and implement real-time data processing pipelines using Apache Spark Streaming.
Improve data quality by leveraging internal tools/frameworks to automatically detect and mitigate data quality issues.
Develop and implement data governance procedures to ensure data security, privacy, and compliance.
Implement new technologies to improve data processing and analysis.
Coach and mentor junior data engineers to enhance their skills and foster a collaborative team environment.
A BE in Computer Science or equivalent with 8+ years of professional experience as a Data Engineer or in a similar role
Experience building scalable data pipelines in Spark using Airflow scheduler/executor framework or similar scheduling tools.
Experience with Databricks and its APIs.
Experience with modern databases (Redshift, Dynamo DB, Mongo DB, Postgres or similar) and data lakes.
Proficient in one or more programming languages such as Python/Scala and rock-solid SQL skills.
Champion automated builds and deployments using CICD tools like Bitbucket, Git
Experience working with large-scale, high-performance data processing systems (batch and streaming)
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Keyskills: GIT data security SCALA Data processing Scheduling Data quality SQL Python Data extraction