Data Engineer Lead

Job highlights

• Experience: 7+ years in data engineering or related roles with hands-on experience in cloud data platforms. . - Education: Bachelor s degree in Computer Science,Engineering,or a related field,or equivalent professional experience. . - Technical Skills: Strong SQL and Python skills for data transformation and automation
• Experience with Snowflake,Databricks,or similar cloud data platforms

Job Summary:

As a Lead Data Engineer, you will play a crucial role in designing, building, and optimizing cloud-based data pipelines and platforms that support enterprise analytics, AI/ML, and business intelligence initiatives.

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Experience: 7+ years in data engineering or related roles with hands-on experience in cloud data platforms.

• Education: Bachelor s degree in Computer Science, Engineering, or a related field, or equivalent professional experience.

• Technical Skills: Strong SQL and Python skills for data transformation and automation. Experience with Snowflake, Databricks, or similar cloud data platforms. Familiarity with orchestration tools (Airflow, dbt, etc.) and data modeling tools (ERWin, E/R Studio). Understanding of data preparation for AI/ML use cases and BI reporting. Exposure to DevOps and agile development practices.

• Leadership & Delivery: Demonstrated ability to lead small teams or mentor junior engineers. Strong problem-solving skills and a proactive mindset. Ability to manage multiple priorities and deliver high-quality results on time.

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Job Responsibilities

Cloud Data Engineering: Develop and maintain scalable, high-performance data pipelines and workflows on cloud platforms such as Snowflake and Databricks.

• Data Modeling & Transformation: Implement dimensional, canonical, and data vault models using tools like dbt and ER modeling platforms to support analytics and reporting.

• Platform Development: Support the development and optimization of enterprise-scale data lakes and data warehouses (100+ TB) for global business operations.

• Data Quality & Governance: Collaborate with data governance teams to implement metadata management, data quality checks, and master data integration.

• Cross-Functional Collaboration: Work closely with data architects, product managers, and agile teams to deliver robust data solutions aligned with enterprise standards.

• Automation & Orchestration: Build and maintain automated ELT/ETL pipelines using tools like Airflow, Fivetran, dbt, and Python, to ensure reliability and efficiency.

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