Data Engineer
Build analytics data models, pipelines, dashboards, and AI-enabled automation for product and operational decision-making. The role requires 2–3+ years of data engineering or analytics experience, advanced SQL, dbt, cloud data warehouse, Python, and BI expertise.
About the job
Responsibilities
- Design, build, and maintain analytics-ready data models, tables, and views in a data warehouse using dbt and Snowflake.
- Develop and automate analytics-layer data pipelines, with data-quality testing and monitoring.
- Partner with product managers, engineers, operations teams, and other stakeholders to translate business needs into data solutions and define and track key metrics.
- Analyze large, complex datasets to identify actionable opportunities for product and operational improvement.
- Develop and maintain ThoughtSpot dashboards and reports for KPIs, product usage, and operational performance.
- Consolidate and harmonize data across business units and acquisitions, maintaining consistent reporting definitions.
- Support or develop tools on the company’s in-house AI platform to improve analytics and automation.
- Document data tools and reporting processes and train business users in self-service analytics.
- Identify and implement analytics workflow and process automation opportunities.
Requirements
- 2–3+ years of relevant experience in data engineering, analytics engineering, or advanced data analytics.
- Advanced SQL proficiency.
- Experience with data modeling, dbt, and cloud data warehouses; Snowflake preferred.
- Experience building analytics-layer pipelines, tables, and views and ensuring data quality.
- Hands-on experience with BI and data visualization tools, including dashboard and report development; ThoughtSpot preferred.
- Experience analyzing complex, high-volume datasets and communicating actionable insights.
- Hands-on Python experience for data analysis or platform and AI-tool integrations.
- Strong written and verbal communication skills and the ability to work cross-functionally.
- Ability to work in a hybrid environment, primarily on-site 3–4 days per week.
Nice to Have
- Experience with experimentation frameworks or statistical analysis, including regression and significance testing.
- Experience in a product-centric or fast-paced environment.
- Stakeholder management experience with Product, Engineering, and Operations teams.
- Familiarity with versioning, testing, and monitoring best practices.
Skills
SQL, dbt, Snowflake, Python, Thoughtspot, Data Modeling, Data Pipelines, Data Quality, Dashboard Development, Data Visualization, Statistical Analysis, Regression
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