Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.
Must Needed Skills: Apache Airflow, Python, PySpark ,Hive(Trino), SQL and Snowflake
- Develop and maintain data pipelines, ELT processes, and workflow orchestration using Apache Airflow, Python, PySpark ,Hive(Trino) and Snowflake to ensure the efficient and reliable delivery of data.
- Design and implement custom connectors to facilitate the ingestion of diverse data sources into our platform, including structured and unstructured data from various document formats .
- Collaborate closely with cross-functional teams to gather requirements, understand data needs, and translate them into technical solutions.
- Design and implement data CI/CD pipelines to enable automated and efficient data integration, transformation, and deployment processes.
- Monitor and troubleshoot data pipelines, proactively identifying and resolving issues related to data ingestion, transformation, and loading.
- Conduct data validation and testing to ensure the accuracy, consistency, and compliance of data.
- Stay up-to-date with emerging technologies and best practices in data engineering.
- Document data workflows, processes, and technical specifications to facilitate knowledge sharing and ensure data governance.
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Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
