Job Description
<p><p><b>About the Company</b><br/><br/>We are seeking a Lead Data Engineer with deep expertise in Snowflake, dbt, and Apache Airflow to design, implement, and optimize scalable data solutions.<br/><br/>This role involves working on complex datasets, building robust data pipelines, ensuring data quality, and collaborating closely with analytics and business teams to deliver actionable insights.<br/><br/>If you are passionate about data architecture, ELT best practices, and modern cloud data stack, wed like to meet you.<br/><br/><b>About the Role :</b><br/><br/>We are seeking a Lead Data Engineer with deep expertise in Snowflake, dbt, and Apache Airflow to design, implement, and optimize scalable data solutions.<br/><br/><b>Responsibilities :</b><br/><br/>- Pipeline Design & Orchestration: Build and maintain robust, scalable data pipelines using Apache Airflow, including incremental & full-load strategies, retries, and logging.<br/><br/>- Data Modelling & Transformation: Develop modular, tested, and documented transformations in dbt, ensuring scalability and maintainability.<br/><br/>Snowflake Development: Design and maintain warehouse in Snowflake, optimize Snowflake schemas, implement performance tuning (clustering keys, warehouse scaling, materialized views), manage access control, and utilize streams & tasks for automation.<br/><br/>Data Quality & Monitoring: Implement validation frameworks (null checks, type checks, threshold alerts) and automated testing for data integrity and reliability.<br/><br/>Collaboration: Partner with analysts, data scientists, and business stakeholders to translate requirements into scalable technical solutions.<br/><br/>Performance Optimization: Develop incremental and full-load strategies with continuous monitoring, retries, and logging and tune query performance and job execution efficiency.<br/><br/>Infrastructure Automation: Use Terraform or similar IaC tools to provision and manage Snowflake, Airflow, and related environments.<br/><br/>Partner with data analysts, scientists, and business stakeholders to translate reporting and analytics requirements into technical :</b><br/><br/>- Bachelors or Masters degree in Computer Science, Information Technology, Data Engineering, or a related field.<br/><br/>710 years of experience in data engineering, with strong hands-on expertise in :<br/><br/></p><p>- Snowflake (data modelling, performance tuning, access control, streams & tasks, external tables)<br/><br/></p><p>- Apache Airflow (DAG design, task dependencies, dynamic tasks, error handling)<br/><br/></p><p>- dbt (modular SQL development, Jinja templating, testing, documentation)<br/><br/>- Proficiency in SQL and Python (Spark experience is a plus).<br/><br/>- Experience building and managing pipelines on AWS, GCP, or Azure.<br/><br/>- Strong understanding of data warehousing concepts and ELT best practices.<br/><br/>- Familiarity with version control (Git) and CI/CD workflows.<br/><br/>- Exposure to infrastructure-as-code tools like Terraform for provisioning Snowflake or Airflow environments.<br/><br/>- Excellent problem-solving, collaboration, and communication skills, with the ability to lead technical projects.<br/><br/><b>Good to have :</b><br/><br/>- Experience with streaming data pipelines (Kafka, Kinesis, Pub/Sub)<br/><br/></p><p>- Exposure to BI/analytics tools (Looker, Tableau, Power BI)<br/><br/></p><p>- Knowledge of data governance and security best practices.<br/></p><br/></p> (ref:hirist.tech)