Job summary

We are looking for a Senior ETL Engineer to design, build, and maintain scalable data pipelines that power analytics, reporting, and data-driven decision making across the organization. This role requires strong SQL and Python expertise, hands-on Databricks experience, and a deep understanding of data and how it is used to drive analytics and insights. You will work closely with data engineers, analytics teams, and business stakeholders to ensure high-quality, reliable, and well-modeled data that elevates our analytics capabilities.

What you will do

• Design, develop, and maintain robust ETL/ELT pipelines using SQL and Python, working with large-scale datasets
• Build scalable and maintainable data workflows leveraging Spark / PySpark and cloud-based data platforms
• Translate business and analytical requirements into high-quality, well-structured datasets
• Apply strong data modeling and data design principles to support downstream analytics and reporting
• Optimize data pipelines for performance, reliability, and cost efficiency
• Ensure data quality, accuracy, and consistency through validation, testing, and monitoring
• Partner closely with analytics and BI teams to enable trusted, analytics-ready data
• Collaborate with analytics teams on reporting and dashboard needs to ensure data accuracy, correctness, and performance
• Troubleshoot and resolve data pipeline failures and data issues end-to-end
• Contribute to architectural decisions, best practices, and data engineering standards
• Participate in code reviews, technical design discussions, and platform improvements
• Mentor junior engineers and help elevate overall data engineering practices
• Document data pipelines, workflows, and operational processes

What you will have

• 5+ years of experience in data engineering, ETL development, or a related role
• Strong proficiency in SQL and Python, with hands-on experience building production-grade ETL pipelines using both
• Hands-on experience with Databricks, Spark, and PySpark
• Experience working with cloud data platforms (AWS, Azure, or GCP)
• Strong understanding of data warehousing concepts, dimensional modeling, and analytics-friendly data design
• A strong sense of data and how it supports analytics, reporting, and business insights
• Experience building batch and incremental data pipelines
• Familiarity with Git, version control, and CI/CD practices
• Strong problem-solving skills and ability to work independently on complex data challenges
• Clear communication skills and ability to collaborate across engineering and analytics teams

Nice to have

• Experience with streaming or near real-time data pipelines (Kafka, Kinesis, etc.)
• Familiarity with data quality, observability, and monitoring tools
• Experience supporting BI tools and analytics use cases
• Exposure to machine learning pipelines or feature engineering
• Prior experience mentoring or guiding junior engineers
• Experience working in Agile or Scrum environments

Benefits

• Generous time off policies
• Top shelf benefits
• Education, wellness and lifestyle support

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