Description

• Architect and scale data platforms- Design and evolve enterprise data platforms, pipelines, and storage systems (warehouses/lakehouses), applying strong data modeling techniques to support scalable analytics across diverse data sources.
• Build robust data pipelines and ingestion frameworks - Develop reusable ETL/ELT frameworks and connectors to ingest and process structured and unstructured data using distributed processing systems (e.g., Spark/PySpark) for batch and streaming workloads.
• Develop production-grade data services - Implement reliable backend systems and RESTful APIs in Python, ensuring best practices in code quality, performance optimization, testing, and maintainability.
• Ensure data reliability, governance, and operational excellence - Manage pipeline orchestration, monitoring, and lifecycle using tools like Airflow, while enforcing data quality, lineage, security, and compliance standards.
• Drive end-to-end data engineering projects, solve complex problems, collaborate with cross-functional teams, and provide technical leadership and mentorship to engineers.

Required Qualifications
• 8+ years of experience in Data Engineering or equivalent combination of education, training, and hands-on experience
• 5+ years of strong hands-on programming experience in Python, with the ability to build production-grade data pipelines, reusable frameworks, and data processing components
• 4+ years of hands on experience in Spark / PySpark, building and optimizing spark pipelines.
• 2+ years of hands on experience building backend systems, implementing RESTful APIs, API versioning, input validation, pagination, and error standards in Python using FastAPI / Django / Flask
• Exposure to data orchestration and pipeline tools (e.g., Airflow, Dagster, or similar)

Key Skills

Education

Any Gradute