Experience Level: 8+ years (with 2–3+ years in Snowflake)
Role Overview:We are seeking a Snowflake Lead Engineer to drive technical delivery for enterprise-scale Snowflake data platform implementations and migrations. This role requires hands-on engineering expertise, project leadership, and a consulting mindset. You will work across multiple client engagements to design scalable data architectures, develop modular data pipelines, and guide teams in adopting modern data stack technologies.
This position is ideal for experienced data engineers with a passion for solving complex data challenges through innovative, scalable solutions.
Key Responsibilities:Lead Snowflake implementation and migration projects for enterprise data platforms.
Design and build reusable data pipelines using DBT, SQL, and Apache Airflow.
Perform advanced data transformation, modeling, and orchestration using Snowflake best practices.
Define and implement data architecture patterns, including batch/incremental loading, data partitioning, and workflow orchestration.
Collaborate with solution architects, PMs, and client stakeholders to translate business needs into technical solutions.
Conduct code reviews, mentor junior engineers, and guide offshore delivery teams.
Optimize Snowflake workloads for performance, cost-efficiency, and scalability.
Troubleshoot and monitor ETL/ELT pipelines and Snowflake workloads using built-in and custom tools.
Work within agile consulting teams across multiple concurrent client projects.
Required Skills & Experience:8+ years of experience in data engineering or related roles.
Minimum 2–3 years of hands-on experience with Snowflake, including data migrations, tuning, and architecture design.
Strong expertise in DBT: models, seeds, snapshots, macros, testing, and documentation.
Advanced SQL skills and deep understanding of query optimization in large-scale environments.
Hands-on experience with ETL/ELT pipeline development, ideally using Apache Airflow.
Proficient in Snowflake-native features: COPY INTO, Snowpipe, Streams/Tasks, Snowpark, etc.
Familiar with Python scripting for automation, transformation, and orchestration.
Experience with CI/CD pipelines, Git, and DevOps best practices.
Hands-on exposure to cloud platforms such as AWS, Azure, or GCP, and cloud-native tools.
Strong communication and leadership skills, with the ability to lead client-facing technical discussions.