
Data lead
4 days ago
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, ₹2,764,456.72 per year
Work Location: In person
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