Snowflake Data Engineer

Job Purpose and Primary Objective

We are seeking a highly experienced and motivated
Snowflake Data Engineer
 to join our dynamic data team. In this pivotal role, you will be the go-to expert for our Snowflake Data Cloud ecosystem. You will take complete ownership of designing, implementing, and optimizing our data architecture, ensuring it is scalable, secure, and efficient. The ideal candidate is a strategic thinker with a hands-on mentality, passionate about establishing best practices and mentoring a culture of technical excellence.

Key Responsibilities

· Architect & Design:
 Lead the design and implementation of end-to-end data solutions on the Snowflake platform. Define the architecture for our enterprise data warehouse and data lakes, including data models, storage strategies, and data integration patterns.

· Technical Ownership:
 Take full ownership of the technical design and implementation of data pipelines (ELT/ETL), ensuring they are robust, performant, and maintainable. Troubleshoot and resolve complex data and infrastructure issues.

· Establish Best Practices:
 Develop, document, and enforce coding standards, development methodologies, and data governance best practices for SQL, Python, and dbt within the Snowflake environment. Champion code reviews and knowledge sharing.

· Performance & Optimization:
 Proactively monitor and tune the Snowflake environment, optimizing for cost, performance, and concurrency. Implement features like clustering keys, materialized views, and query optimization techniques.

· DevOps & Automation:
 Integrate and promote DevOps principles for data engineering (DataOps). Implement and manage CI/CD pipelines for database objects and data pipelines using tools like Git, dbt, and Jenkins/Azure DevOps.

· Collaboration & Mentorship:
 Work closely with data analysts, data scientists, and business stakeholders to understand requirements and translate them into technical solutions. Mentor junior engineers and provide technical guidance to the team.

Required Skills & Qualifications

· Experience: 5
+ years of experience with hands-on experience in data engineering, recent experience designing and building solutions on the
Snowflake
 platform.

· Snowflake Expertise:
 Deep, practical knowledge of Snowflake architecture and its ecosystem, including Snowpipe, Streams, Tasks, Zero-Copy Cloning, Time Travel, Dynamic Data Masking, and Role-Based Access Control (RBAC).

· Expert SQL Skills:
 Mastery of advanced SQL, including complex joins, window functions, and performance tuning analytical queries.

· Data Modeling:
 Proven experience with data modeling techniques (e.g., Kimball, Inmon, Data Vault) and translating business requirements into logical and physical data models.

· ETL/ELT & Programming:
 Strong proficiency with modern data integration tools (e.g.,
dbt
, Matillion, Fivetran, Airflow) and programming languages, primarily
Python
, for data processing.

· DevOps Mindset:
 Solid experience with version control systems (
Git
) and implementing CI/CD pipelines for data workflows. Familiarity with Infrastructure as Code (e.g., Terraform) is a plus.

· Cloud Proficiency:
 Hands-on experience with at least one major cloud provider (
AWS, Azure, or GCP
), particularly their storage and compute services relevant to data pipelines.

Nice to Have

· Certification:
 Snowflake SnowPro Core or Advanced certifications are highly desirable.

· Data Governance:
 Experience implementing data governance and data quality frameworks.

· Big Data Technologies:
 Familiarity with technologies like Apache Spark or Kafka.

· BI & Visualization:
 Experience supporting BI tools like Tableau or Power BI connected to Snowflake.

Leadership:
 Prior experience in technical lead or mentorship capacity