Job Title: Lead Data Engineer – SnowflakeLocation: Bangalore
Role OverviewWe are seeking an experienced Lead Data Engineer with strong expertise in Snowflake and dbt, to design and implement scalable, high-performing data solutions. The ideal candidate will have proven experience in building modern data pipelines, mentoring engineering teams, and driving best practices for cloud-based data platforms.Key Responsibilities
- Lead the design, development, and optimization of data pipelines and data warehouse solutions on Snowflake.
- Develop and maintain dbt models for data transformation, testing, and documentation.
- Collaborate with cross-functional teams including data architects, analysts, and business stakeholders to deliver robust data solutions.
- Ensure high standards of data quality, governance, and security across pipelines and platforms.
- Leverage Airflow (or other orchestration tools) to schedule and monitor workflows.
- Integrate data from multiple sources using tools like Fivetran.
- Provide technical leadership, mentoring, and guidance to junior engineers in the team.
- Optimize costs, performance, and scalability of cloud-based data environments.
- Contribute to architectural decisions, code reviews, and best practices.
Required Skills & Experience
- 8–12 years of overall experience in Data Engineering, with at least 3–4 years in a lead role.
- Strong hands-on expertise in Snowflake (data modeling, performance tuning, query optimization, security, and cost management).
- Proficiency in dbt (core concepts, macros, testing, documentation, and deployment).
- Solid programming skills in Python (for data processing, automation, and integrations).
- Experience with workflow orchestration tools such as Apache Airflow.
- Exposure to ELT/ETL tools like Fivetran.
- Strong understanding of modern data warehouse architectures, data governance, and cloud-native environments.
- Excellent problem-solving, communication, and leadership skills.
Good to Have
- Hands-on experience with Databricks (PySpark, Delta Lake, MLflow).
- Exposure to other cloud platforms (AWS, Azure, or GCP).
- Experience in building CI/CD pipelines for data workflows.
Work Location: In person
