Snowflake Data Engineer

Role & responsibilities

Role Overview

We 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

• 812 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.