Posted:19 hours ago|
Platform:
On-site
Full Time
Data Pipeline Development:
Design, develop, and optimize scalable data pipelines using Databricks, Snowflake, and Azure Data Factory (ADF).
Implement ETL/ELT processes for structured and unstructured data across data lakes and warehouses.
Data Modeling & Optimization:
Create efficient data models (e.g., star schema, snowflake schema) for Snowflake to ensure optimal query performance.
Optimize Databricks workflows for performance tuning and scalability.
Integration & Collaboration:
Integrate Snowflake with other systems (e.g., ADLS Gen2, Salesforce) and configure APIs for seamless data flow.
Collaborate with cross-functional teams, including data scientists, analysts, and infrastructure engineers, to deliver end-to-end solutions.
Automation & CI/CD:
Develop reusable jobs and implement CI/CD pipelines using GitHub Actions or Azure DevOps.
Automate source-to-target mappings and data lineage tracking using tools like Collibra.
Troubleshooting & Documentation:
Identify and resolve performance bottlenecks in Snowflake queries or Databricks jobs.
Document workflows, processes, and technical solutions to ensure clarity and maintainability.
Required Skills
Hands-on experience with Snowflake, including schema design, query optimization, and SnowSQL scripting.
Proficiency in Databricks, including PySpark workflows and distributed computing frameworks.
Strong knowledge of ETL/ELT processes and tools like Azure Data Factory (ADF) or DBT Labs.
Expertise in SQL for data manipulation and transformation; familiarity with NoSQL databases is a plus.
Experience integrating cloud platforms such as Azure or AWS with Snowflake/Databricks.
Familiarity with CI/CD pipelines using GitHub Actions or Azure DevOps.
Qualifications
Bachelor’s degree in Computer Science, Information Systems, or a related field.
3–5 years of experience as a Database Engineer or Data Engineer working with Snowflake and Databricks.
Certifications in Snowflake or Databricks are a plus (e.g., SnowPro Certification).
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