Develop, maintain, and optimize ETL pipelines to ingest, transform, and load data from multiple sources
Write efficient, scalable, and well-documented Python scripts for data processing and automation
Design and optimize complex SQL queries, views, and stored procedures for performance and accuracy
Perform data validation, quality checks, and reconciliation to ensure data integrity
Troubleshoot and resolve data pipeline failures and performance issues
Collaborate with data analysts, data scientists, and business teams to understand data requirements
Implement best practices for data security, governance, and compliance
Maintain technical documentation for ETL workflows and data models

Experience with cloud platforms (AWS, Azure, or GCP)
Familiarity with big data technologies (Spark, Hadoop)
Exposure to workflow schedulers (Airflow, Prefect, Cron)
Knowledge of CI/CD pipelines and version control (Git)
Understanding of data governance and lineage tools

Strong proficiency in Python (data manipulation, scripting, automation)
Advanced knowledge of SQL (joins, subqueries, window functions, performance tuning)
Hands-on experience with ETL/ELT tools or frameworks
Experience working with relational databases (e.g., MySQL, PostgreSQL, SQL Server, Oracle)
Solid understanding of data warehousing concepts (fact/dimension tables, star schema)
Ability to work with large datasets and optimize data processing workflows
Strong analytical, problem-solving, and debugging skills

We are looking for a skilled Python, SQL, and ETL Developer to design, build, and maintain scalable data pipelines and data integration solutions. The ideal candidate will work closely with data engineers, analysts, and business stakeholders to ensure reliable, high‑quality data delivery for reporting, analytics, and downstream applications.