Senior Business Intelligence Engineer, Enterprise Intelligence

Jobtailor; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Jobtailor is seeking an analytics engineer to design, build, and maintain governed KPI models that serve dashboards, reports, and AI agents from a single source of truth.

You will own automation of executive scorecards, lead metric governance, and create semantic data layers with SQL on Snowflake or Databricks, while partnering with Product, Engineering, Finance, GTM, and Customer Success to translate business priorities into data-driven recommendations.

Qualifications

• Bachelor's degree in CS/engineering/math/statistics or related quantitative field.
• 5+ years in analytics engineering, BI engineering, data engineering, or similar.
• Experience designing KPIs and generating insights to drive decisions.
• Familiarity with dbt and YAML-based metric definitions; hands-on preferred.
• Strong SQL expertise with Snowflake and/or Databricks.

Responsibilities

• Design, build, and maintain governed metric models for KPI definitions.
• Automate corporate scorecards and executive reporting with AI anomaly detection.
• Deep-dive on KPIs and business goals; translate priorities into data-driven recommendations.
• Lead metric certification and governance, standardising dashboards.
• Build AI agent integrations to query data via semantic layer with metric SQL.
• Enable self-serve analytics with docs, dashboards QA, and governance across platforms.
• Collaborate with Product, Engineering, Finance, GTM, and Customer Success to align metrics.
• Translate UX signals and customer feedback into measurable data pipelines.

Skills

KPI design

Cross-functional collaboration

Async communication

Ownership mindset

Education

Bachelor's degree in Computer Science / Engineering / Mathematics / Statistics

Tools

dbt

SQL

Snowflake

Databricks

Tableau

Amplitude

MCP

LLM tooling

Job description

Responsibilities

• Design, build, and maintain governed metric models — standardised, reusable definitions of business KPIs that dashboards, reports, and AI agents all query from a single source of truth.
• Own the automation and maintenance of corporate scorecards and executive reporting (SLT/XLT), ensuring trusted, timely data with AI-powered anomaly detection and alerting.
• Conduct deep dives on key KPIs and company goals, surfacing actionable insights and translating short- and long-term business priorities into data‑driven recommendations shared via reviews and async updates.
• Lead metric certification and governance — maintain a centralised KPI glossary, standardise dashboards, and retire stale reporting assets.
• Build AI agent integrations (MCP servers, skills, subagents) that allow tools like Claude and Snowflake Cortex to query enterprise data through the semantic layer with metric‑correct SQL.
• Drive self‑serve analytics enablement — documentation, training, dashboard quality checks, and experimentation governance across Amplitude, Tableau, and other platforms.
• Partner with Product, Engineering, Finance, GTM, and Customer Success to align metric definitions and data sources cross‑functionally.
• Translate qualitative UX signals and customer feedback into measurable data pipelines.

Requirements

• Bachelor's degree (B.Tech / B.E. / B.Sc.) in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
• 5+ years of experience in analytics engineering, BI engineering, data engineering, or a closely related role.
• Experience with KPI design and insight generation — ability to define meaningful metrics, analyse trends, identify anomalies, and communicate findings that drive business decisions.
• Familiarity with dbt (dbt Core or dbt Cloud) and concepts like YAML-based metric definitions and reusable data models; hands‑on experience is a plus but not required.
• Deep SQL expertise with experience working in cloud data platforms such as Snowflake and/or Databricks.
• Experience building semantic layers, governed data models, or canonical metric definitions for downstream consumption.
• Working familiarity with AI/LLM tooling — concepts such as MCP (Model Context Protocol), agentic frameworks, or tool‑use patterns for large language models.
• Proficiency with one or more BI/visualisation tools (Tableau, Amplitude, or similar).
• Strong cross‑functional collaboration skills and excellent async communication across time zones (IST / US Pacific overlap expected).
• A proactive, ownership‑driven mindset — comfortable building new processes and standards in a greenfield environment.

Core Competencies

Demonstrates expertise in analytics engineering and BI engineering, with a strong focus on KPI design, data governance, and the ability to translate business priorities into actionable insights. Proficient in SQL and familiar with AI/LLM tooling, ensuring effective cross‑functional collaboration and communication.