Data Engineer
Summary
Designs and builds scalable data pipelines in Python, PySpark, and SQL to extract, transform, and load large datasets for analytics and ML, using cloud platforms like Azure/AWS and tools such as Airflow and Databricks.
Experience: 10+ Years
Duration-6+ Months
- Strong proficiency in Python, including Pandas, NumPy, PySpark, with hands on experience using Impala.
- Proven experience working on Hadoop based platforms, performing large scale data extraction, transformation, and processing.
- Strong SQL skills and experience working with both relational and distributed data stores.
- Experience with enterprise data platforms and business intelligence ecosystems.
- Hands on experience with ETL / ELT and data integration tools, such as Apache Airflow, Apache NiFi, Azure Data Factory.
- Experience in data modelling, querying, data mining, and reporting over large volumes of granular data.
- Exposure to machine learning concepts and analytical techniques used in advanced data solutions and Feature calculations and Model serving is a big plus.
- 8+ years of experience in data engineering, big data analytics, or enterprise data platforms, including 2+ years in a lead or technical leadership role.
- Experience working with cloud based data platforms (Azure/AWS, Databricks/Snowflake), including data lakes, distributed compute, and storage services.
- Experience implementing CI/CD pipelines and DevOps practices for data engineering workflows.
GenAI / LLM Skills (Preferred)
- Experience enabling GenAI/AI products through scalable, reliable data ingestion and transformation pipelines (batch and streaming).
- Exposure to unstructured and semi-structured data processing (documents/logs/text) and building curated datasets for downstream consumption.
- Strong understanding of data governance, privacy, and security requirements when using enterprise data with AI (PII handling, access control, auditability).
- Familiarity with operationalizing AI data workflows (monitoring, data quality checks, reproducibility, and cost-aware scaling in cloud environments).