Sr. Analytics Engineer- Hybrid in Bangalore

Smartsheet Smartsheet · Seattle · India · Business Intelligence & Ops

This role focuses on building and maintaining a governed, reliable, and scalable analytics system, transforming raw data into usable datasets for DS/ML, analytics, and reporting. It involves leading the architecture and implementation of data transformation systems, fostering data democratization, and ensuring data accessibility. The role also includes creating data testing plans and collaborating on data privacy needs.

What you'd actually do

  1. Leading the implementation of efficient and scalable systems that optimize data transformations from our data lake in Snowflake into clean, documented data products.
  2. Contribute to the design standards and architectural principle standards, designs and processes.
  3. Build and maintain a governed, robust, reliable, and scalable analytics system (DW/DV).
  4. Collaborating with BI analysts and data scientists to provide proper data accessibility guidelines.
  5. Foster data democratization via activation of data on critical platforms like Smartsheet platform, Amplitude, Thoughtspot , Gainsight.

Skills

Required

  • 6+ 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.

What the JD emphasized

  • advanced data modeling using Data Vault (DV 2.0), dbt, and Snowflake
  • building analytics systems with data transformation, dependency, and workload management (Data Vault, AutomateDV, dbt core, Airflow, Snowflake, GitLab, SQL)
  • building and maintaining business analytics layers and semantic layers
  • supporting and maintaining analytics/data warehouse solutions with mission-critical datasets
  • data governance principles
  • data privacy needs
  • industry compliance standards (e.g., GDPR, HIPAA, CCPA)