. Design and maintain scalable ETL pipelines to process structured and unstructured data from
multiple sources.
. Build and optimize data warehouses, data lakes, and data marts to support downstream analytics
and AI systems.
. Collaborate closely with data scientists and AI engineers to ensure clean, accessible, and reliable
data.
. Implement data quality checks, monitoring, and logging to ensure data integrity and lineage.
. Automate data workflows using tools like Airflow, dbt, or custom Python scripts.
. Work with both structured (MySQL, PostgreSQL) and non-structured (pdf, images, ppt and many
more) data sources.
. Ensure secure, efficient, and governed access to enterprise-grade datasets and APIs.
. Continuously improve data infrastructure based on usage feedback, scalability needs, and
business goals.
. Proficient in SQL and one programming language (Python, Scala, or Java)
. Solid understanding of data modeling, ETL design, and batch/streaming data processing
. Hands-on experience with modern data pipeline tools (e.g., Apache Airflow, dbt, or similar)
. Familiar with both relational (PostgreSQL, MySQL) and non-relational (MongoDB, S3, etc.)
databases
. Experience working with large datasets and optimizing performance for data queries and storage
. Comfortable collaborating with data scientists, analysts, and backend engineers
