Enterprise Data Engineer

Key Responsibilities:

• Design, build, and maintain ETL pipelines to extract, transform, and load structured and unstructured data into data lakes and enterprise systems.
• Manage Hadoop-based data platforms , using tools like Hive, Spark, HDFS, and Talend for data ingestion and transformation.
• Develop and optimize SQL/HiveQL queries for data wrangling, aggregation, and reporting.
• Support data integration from CRM systems, sales logs, and third-party sources for analytics and reporting purposes.
• Collaborate with business analysts and stakeholders to understand data requirements and translate them into effective data models and pipelines.
• Build and maintain metadata extraction and schema management using Python/Unix scripting.
• Work closely with BI teams to ensure Power BI dashboards are connected to accurate and timely datasets.
• Implement data validation and quality assurance routines during pipeline development and data loading processes.
• Participate in sprint-based Agile teams, supporting development, testing, and deployment efforts across projects.

Requirements:

• Minimum 3–5 years of experience in data engineering, ETL development, or enterprise data platforms.
• Proficient in Hadoop ecosystem tools : Hive, Spark, HDFS, Talend, and Control-M.
• Strong command over SQL (Oracle, Teradata, MS SQL) and experience in developing DDL/DML scripts .
• Hands-on scripting experience in Python and Unix Shell for automation and data processing.
• Exposure to Power BI for data visualization and reporting is a plus.
• Good understanding of data warehousing concepts , data modeling , and data governance principles .
• Excellent analytical, problem-solving, and communication skills.