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Fullstack Data Engineer
Location
India
Posted
198 days ago
Salary
0
Seniority
Senior
Job Description
Fullstack Data Engineer
Codvo.ai
• Design and develop ETL/ELT pipelines on platforms like Databricks (PySpark, Delta Lake, SQL), Informatica, Teradata, Snowflake • Architect data models (batch and streaming) for analytics, ML, and reporting • Optimize performance of large-scale distributed data processing jobs • Implement CI/CD pipelines for Databricks workflows using GitHub Actions, Azure DevOps, or similar • Build and maintain APIs, dashboards, or applications that consume processed data (full-stack aspect) • Collaborate with data scientists, analysts, and business stakeholders to deliver solutions • Ensure data quality, lineage, governance, and security compliance • Deploy solutions across cloud environments (Azure, AWS, or GCP)
Job Requirements
- 4–7 years of experience in data engineering, with deep expertise in Databricks
- Bachelor's or Master’s in Computer Science, Data Engineering, or related field
- Strong in PySpark, Delta Lake, Databricks SQL
- Experience with Databricks Workflows, Unity Catalog, and Delta Live Tables
- Python (mandatory), SQL (expert)
- Exposure to Java/Scala (for Spark jobs)
- Knowledge of APIs, microservices (FastAPI/Flask), or basic front-end (React/Angular) is a plus
- Proficiency with at least one: Azure Databricks, AWS Databricks, or GCP Databricks
Benefits
- Flexible work arrangements
- Professional development
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• Translate business requirements to data engineering solutions to support an enterprise scale AWS and/or Databricks based data environments. • Support designing, maintaining, automating and optimizing ETL operations ensuring data quality and efficient data management. • Design, build, and optimize scalable data solutions using a Medallion Architecture. • Manage ingestion routines for processing multi-terabyte datasets efficiently for multiple projects simultaneously. • Integrate data from various structured and unstructured sources to enable high-quality business insights. • Implement effective data management strategies to ensure data integrity, availability, and accessibility. • Identify opportunities for cost optimization in data storage, processing, and analytics operations. • Monitor and support user requests, addressing platform or performance issues, cluster stability, Spark optimization, and configuration management. • Collaborate with the team to enable advanced AI-driven analytics and data science workflows.

