Product Data Analyst

ThinkWise Consulting LLP; Hyderabad; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

ThinkWise Consulting LLP is seeking a skilled analytics professional to support digital products through building and maintaining dashboards in DOMO and Power BI. The ideal candidate will have 5–7 years of experience in product analytics and be able to independently analyze data, communicate findings effectively, and manage stakeholder expectations.

The role involves working in DOMO, Snowflake, and contributing to data modeling efforts. Strong SQL skills and familiarity with various analytics tools are essential for success.

Qualifications

• 5–7 years of professional experience in analytics roles.
• Hands-on experience in DOMO and Snowflake data environments.
• Ability to communicate technical findings to non-technical audiences.

Responsibilities

• Build, ship, and maintain production-quality dashboards.
• Turn ambiguous business questions into actionable insights.
• Define and document key product and eCommerce metrics.

Skills

Building dashboards and reports

Strong SQL skills

Excellent communication

Prioritization and workload management

Data modeling

Tools

DOMO

Power BI

Snowflake

Job description

Mode of interview - 1st round Face-To-Face

Required Qualifications

• 5–7 years of professional experience in a product analytics, business analytics, or data analyst role supporting digital products, eCommerce, or B2B platforms.
• Demonstrated ability to build, ship, and maintain production-quality dashboards and reports in at least one of: DOMO, Google Looker (LookML), or Power BI with working familiarity across more than one preferred.
• Hands‑on experience working in DOMO and Snowflake data environments, including writing strong, performant SQL against large data sets.
• Track record of independently turning ambiguous business questions into structured analyses and clear, actionable insights you can dig into the data without being told exactly where to look.
• Excellent written and verbal communication skills, with the ability to translate technical findings for non-technical audiences and senior leaders.
• Demonstrated experience prioritizing across multiple stakeholders, negotiating scope and timelines, and managing your own workload to deliver consistently.

Preferred Qualifications

• Experience supporting product teams in a manufacturing, industrial, or technical eCommerce environment ideally with exposure to digital quoting, configurator‑driven workflows, or custom‑manufactured goods (e.g., CNC, injection molding, 3D printing, sheet metal).
• Familiarity with web and product analytics tooling (e.g., Google Analytics 4, Adobe Analytics, Amplitude, Mixpanel, Heap) and event‑level data.
• Working knowledge of statistical concepts, A/B testing, and experimentation frameworks.
• Comfort with Python or R for deeper, ad‑hoc analysis beyond SQL.
• Experience contributing to data modeling work (e.g., dbt, semantic layers, LookML) in partnership with a central Data & Analytics team.
• Design, build, and maintain dashboards and self‑service reports in DOMO, Google Looker, or Power BI to give product managers and stakeholders a reliable, real‑time view of the business.
• Define, document, and govern key product and eCommerce metrics so the organization speaks a common language about performance.
• Continuously improve existing reports sunsetting what is no longer used, consolidating overlap, and ensuring what remains is accurate, performant, and easy to understand.
• Work fluently across our DOMO and Snowflake data environments writing performant SQL, validating data quality, and understanding the lineage behind the metrics you publish.
• Partner with the Data & Analytics team to define data requirements, refine data models, and advocate for the upstream changes needed to support reliable product analytics.
• Help establish and follow standards for analytical work documentation, version control, naming conventions, and reproducibility.
• Understand and navigate highly independent datasets, with the ability to assess schema differences, metric definitions, and join logic to avoid misalignment, double‑counting, or misleading outputs.
• Validate joins, aggregations, and filters through spot checks, reconciliation against known benchmarks, and peer review before insights or charts are shared with stakeholders.