Job Description
Key Responsibilities :
– Design and implement scalable and reliable data pipelines using ETL tools and cloud-based technologies to ingest, process, and transform large datasets for various analytical and reporting needs.
– Develop and maintain data models and data warehouses to ensure data integrity, consistency, and accessibility for data scientists and business users.
– Lead and mentor a team of data engineers, providing technical guidance and fostering a collaborative and innovative work environment to enhance team performance and skill development.
– Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data requirements and deliver data solutions that meet business objectives.
– Implement and enforce data governance policies and procedures to ensure data quality, security, and compliance with regulatory requirements, safeguarding sensitive information and maintaining data integrity.
– Optimize data infrastructure and pipelines for performance and scalability to support growing data volumes and evolving business needs, ensuring efficient data processing and delivery.
– Evaluate and implement new data technologies and tools to improve data processing capabilities and enhance the overall data infrastructure, staying abreast of industry trends and best practices.
– Ensure the security of data assets by implementing appropriate security measures and access controls to protect sensitive information and prevent unauthorized access.
Required Skillset :
– Demonstrated ability to design and implement complex data warehousing solutions using various data modeling techniques and ETL processes.
– Proven expertise in writing complex SQL queries and optimizing database performance for efficient data retrieval and manipulation.
– Proficient in programming with Python and Pyspark to develop data pipelines and perform data transformations.
– Strong understanding of data architecture principles and best practices for building scalable and reliable data systems.
– Experience in implementing data governance policies and procedures to ensure data quality, security, and compliance.
– Hands-on experience with cloud platforms such as AWS, including services like S3, EC2, Glue, and Redshift.
– Excellent communication and interpersonal skills to effectively collaborate with cross-functional teams and stakeholders.
– Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
– Ability to adapt to a fast-paced and dynamic work environment, embracing new technologies and methodologies.
– 10-12 years of relevant experience in Data Engineering.
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