Sr. Analytics Engineer

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Requirements:

• 10+ years of experience in advanced data modeling using Data Vault (DV 2.0), dbt, and Snowflake.
• Proven experience in building analytics systems with data transformation, dependency, and workload management (Data Vault, AutomateDV, dbt core, Airflow, Snowflake, GitLab, SQL).
• Proven experience with building and maintaining business analytics layers and semantic layers to enable self-service for non-technical end-users.
• Strong production experience supporting and maintaining analytics/data warehouse solutions with mission-critical datasets.
• Familiarity with data governance principles for maintaining datasets to enable a single source of truth.
• Familiarity with self-serve product analytics tools (Amplitude, Thoughtspot, Tableau, Gainsight, etc.).
• Knowledge of code management and CI/CD practices (GitLab, Airflow, Snowflake, Terraform).
• Familiarity with AWS solutions such as S3, EC2, VPC.

Responsibilities:

• Leading the implementation of efficient and scalable systems that optimize data transformations from our data lake in Snowflake into clean, documented data products.
• Contribute to the design standards and architectural principle standards, designs and processes.
• Build and maintain a governed, robust, reliable, and scalable analytics system (DW/DV).
• Collaborating with BI analysts and data scientists to provide proper data accessibility guidelines.
• Foster data democratization via activation of data on critical platforms like Smartsheet platform, Amplitude, Thoughtspot , Gainsight.
• Create and contribute to frameworks that improve data quality and observability to identify and resolve issues faster.
• Create and implement data testing plans, encompassing data validation, data quality assessment, and regression testing, to identify anomalies, inconsistencies, or errors in produced datasets.
• Mentor and guide teams and help drive high standards across the team by conducting design reviews and engineering ideation forums.
• Collaborate with cross-functional teams to assess data privacy needs and define data masking strategies aligned with industry compliance standards (e.g., GDPR, HIPAA, CCPA).