Data Engineer

• Design & Development: Lead the design, development, and implementation of scalable, high-performance, and reliable data pipelines using various ETL/ELT tools and programming languages (e.g., Python, Scala).
• Data Modeling: Develop and optimize data models (dimensional, relational, columnar) for efficient storage, retrieval, and analysis of large datasets.
• Infrastructure Management: Build, maintain, and optimize data warehousing solutions (e.g., Snowflake, Redshift, BigQuery, Databricks) and data lakes (e.g., S3, ADLS).
• Performance Optimization: Identify and resolve performance bottlenecks in data pipelines and queries, ensuring optimal data flow and accessibility.
• Data Governance & Quality: Implement and enforce data governance best practices, ensuring data quality, integrity, security, and compliance.
• Collaboration: Work closely with data scientists, data analysts, and product managers to understand data requirements and translate them into technical solutions.
• Automation: Automate data extraction, transformation, loading (ETL/ELT) processes, monitoring, and alerting.
• Mentorship & Leadership: Mentor junior data engineers, provide technical guidance, and contribute to the overall growth and best practices of the data engineering team.
• Innovation: Stay up-to-date with emerging data technologies and trends, evaluating and recommending new tools and approaches to improve our data platform.
Documentation: Create and maintain comprehensive documentation for data pipelines, models, and processes.