Data lead

Data Infrastructure & Pipeline Development: ● Design, develop, and optimize scalable, efficient, and reliable data pipelines for large-scale data processing and transformation. ● Manage and maintain data architecture, ensuring high availability and performance using tools like Snowflake, Dataproc, BigQuery and other cloud technologies. ● Lead the integration of data sources from multiple systems, ensuring seamless data flow across various platforms.

● Build and optimize data pipelines using BigQuery, Snowflake, DBT Cloud, and Airflow.

● Expertise in Data Modelling to desing and build Data warehouses, Data Marts and Data lakes

● Manage version control and workflows with GitHub. Performance & Optimization:

● Perform tuning and optimization of queries and data pipelines to ensure high-performance data systems.

● Conduct regular performance reviews and recommend improvements or optimizations for system reliability, speed, and cost-efficiency. DBT (Data Build Tool) Implementation:

● Implement and maintain DBT models for data transformation workflows.

● Collaborate with data analysts and data scientists to ensure high-quality, well-documented datasets for downstream analysis.

● Ensure the use of best practices for DBT testing, version control, and deployment. Snowflake Management:

● Architect and optimize Snowflake data warehouse environments.

● Oversee and manage Snowflake data ingestion, transformation, and storage strategies.

● Collaborate with cross-functional teams to ensure Snowflake is being utilized effectively and efficiently. Leadership & Mentorship:

● Lead and mentor a team of data engineers, ensuring that best practices are followed in development and deployment of data pipelines.

● Conduct code reviews, provide feedback, and ensure the implementation of high-quality data solutions.

Preferred Skills:

● 10+ years of experience in Data Engineering with a strong focus on data warehousing, ETL pipelines, and big data technologies.

● At least 3-5 years of hands-on experience with Snowflake data warehouse or BigQuery, including setup, configuration, optimization, and maintenance.

● Proficiency in SQL for query optimization and performance tuning.

● In-depth experience with Dataproc for running large-scale data processing workflows (e.g., Spark, Hadoop).

● Expertise with DBT or any other ELT tool for data transformation and model building. Technical Skills:

● Strong experience in cloud platforms like AWS, GCP, or Azure, with a focus on data engineering tools and services.

● Proficient in programming/scripting languages such as Python, Java, or Scala for data processing.

● Experience with CI/CD pipelines and version control (Git, Jenkins, etc.).

● Knowledge of distributed computing frameworks (e.g., Spark, Hadoop) and related data processing concepts. Data Architecture & Design:

● Experience with building and maintaining data warehouses and lakes.

● Strong understanding of data modeling concepts, data quality, and governance.

● Familiarity with Kafka, Airflow, or similar tools for orchestrating data workflows.

Job Types: Full-time, Permanent, Fresher

Pay: ₹2,540,454.62 - ₹2,764,456.72 per year

Work Location: In person