Data Analyst

Evlo AI; Washington; On-site; USD 110,000 - 160,000; Full time

Job summary

Tech Ladies, Inc. is seeking a data analytics professional to merge analytics with data infrastructure. You will own semantic metrics, build dashboards, and partner with analytics and data engineers to deliver trustworthy, reproducible reports.

Responsibilities include designing Looker/Tableau dashboards, writing advanced SQL for Snowflake, and developing dbt models. Strong communication with stakeholders is essential for driving data-informed decisions.

Qualifications

• 3–6 years of experience in data analytics or BI at a product-driven company.
• Expert-level SQL with window functions, CTEs, and performance tuning.
• Hands-on dbt and a modern cloud data warehouse (Snowflake/BigQuery/Redshift).
• Python or R for analysis; familiarity with pandas/matplotlib libraries.
• Strong stakeholder management; translate questions into analyses.
• Bachelor’s degree in a quantitative field or equivalent experience.
• Bonus: A/B testing frameworks, LookML exposure, event analytics tools like Amplitude/Mixpanel.

Responsibilities

• Design and build dashboards and self-serve reporting for weekly decisions.
• Write complex SQL against a Snowflake warehouse and optimize large queries.
• Develop and maintain dbt models, tests, and documentation in a versioned repo.
• Perform deep-dive analyses on retention, funnel, pricing, and marketing spend.
• Define and implement consistent metric definitions across the semantic layer.
• Create Python-based analyses and automations to streamline reporting.
• Identify data quality issues and implement proactive checks with data engineering.

Skills

SQL proficiency

Python or R for analysis

Stakeholder management

Data analysis

Communication of insights

Education

Bachelor's degree in a quantitative field

Tools

dbt

Snowflake

BigQuery

Redshift

Looker

Tableau

Job description

About The Role

The role sits at the intersection of analytics and data infrastructure - turning large, messy datasets across the product, growth, and operations domains into models, metrics, and dashboards that leadership and product teams actually use to make decisions.

You will work closely with analytics engineers and data engineers to own the semantic layer, define core business metrics, and ensure that every number the organization reports is trustworthy, documented, and reproducible.

Key Responsibilities

• Design and build dashboards and self-serve reporting in Looker or Tableau that leadership and product teams use for weekly decision-making
• Write complex SQL against a Snowflake warehouse, optimizing queries over large, partitioned, and incrementally loaded tables
• Develop and maintain dbt models, tests, and documentation as part of a version-controlled analytics codebase
• Conduct deep-dive analyses on retention, funnel conversion, pricing, and marketing spend; communicate findings clearly to non-technical stakeholders
• Partner with analytics engineers to define and implement consistent metric definitions across the semantic layer
• Build lightweight Python-based analyses and automations to replace manual reporting workflows
• Proactively identify data quality issues, root-cause them with data engineering, and implement checks to prevent recurrence

What We Are Looking For

• 3–6 years of experience in data analytics, business intelligence, or a comparable analytical role at a product-driven company
• Expert-level SQL, including window functions, CTEs, and query performance tuning against large datasets
• Hands-on experience with dbt and a modern cloud data warehouse (Snowflake, BigQuery, or Redshift)
• Proficiency in Python or R for analysis, with comfort using pandas, matplotlib, or equivalent libraries
• Strong stakeholder management skills - able to translate ambiguous business questions into rigorous, actionable analyses
• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience
• Bonus: experience with A/B testing frameworks and causal inference methods; exposure to Looker's LookML; familiarity with event analytics tools like Amplitude or Mixpanel