About the role -

We are looking for a business-facing Analytics Data Modeler to act as the bridge between stakeholders and our data & engineering teams. Youll own the middle layer: understanding business needs, translating them into clear technical requirements, and designing the data models and specifications that enable robust enterprise reporting and analytics. This is a hybrid between a data modeler and an analyst: hands-on with SQL and data profiling, but also leading conversations with the business to shape what we build.

What youll do (Business & requirements*)-

• Work closely with business stakeholders to understand goals, KPIs, and processes.

• Lead workshops and interviews to gather and structure reporting/analytics requirements.

• Translate business needs into precise functional and technical requirements.

• Define and maintain business definitions for core entities, metrics, and KPIs.

• Data modeling & design (no report building)*

• Design logical and physical data models for the enterprise reporting layer (e.g., star schemas, dimensional models, semantic entities).

• Create and maintain source-to-target mappings and transformation specifications.

• Define data contracts and interface specifications between source systems, the warehouse, and BI layers.

• Work closely with data engineers and BI developers so that models are implemented correctly and efficiently.

• Hands-on data work (SQL & analysis)*

Use SQL to explore and profile source data, validate assumptions, and assess data quality.

• Write SQL to prototype data transformations and models (e.g., views, CTEs, dbt models, or equivalent in your stack).

• Trace metrics and attributes end-to-end: from business definition to underlying source fields and logic.

• Support engineers and BI developers with clear specifications, test cases, and acceptance criteria.

• Governance, quality & documentation*





• Own functional and technical documentation for your domains (definitions, models, mappings, logic).

• Help establish and enforce standards for modeling, naming, and metric definitions.

• Contribute to data quality rules and acceptance tests for critical datasets.

• Act as a subject-matter expert for what data means and how it should be modeled.

What you’ll bring (Must-haves)-

• Experience in a BI / analytics-oriented role (e.g., BI Analyst, Analytics Engineer, Data Modeler, Functional Analyst, or similar).

• Strong SQL skills; comfortable with complex joins, aggregations, and investigative queries.

• Practical experience designing data models for analytics and reporting (dimensional/star schema, fact/dimension concepts, slowly changing dimensions, etc.).

• Demonstrated ability to gather and refine business requirements and convert them into technical specs and models.

• Strong communication skills with the ability to talk to both business stakeholders and technical teams.

• Proactive, hands-on mindset; you take ownership, ask questions, and drive clarity rather than waiting for perfect input. Nice-to-haves (adapt to your environment):

• Experience with enterprise data warehouses and modern cloud platforms (e.g., Snowflake, BigQuery, Redshift, Azure Synapse).

• Familiarity with data modeling tools (e.g., ER/Studio, ERwin, db docs, Lucidchart, etc.).

• Understanding of ETL/ELT concepts and how pipelines support your models (without necessarily building the pipelines yourself).

• Experience in [your industry] and knowledge of common KPIs and data domains. What success looks like

• Stakeholders see their needs accurately reflected in data models and validated datasets.

• Data engineers and BI developers have transparent, unambiguous specifications and models to build from.

• Core entities and metrics are consistently defined, reducing rework and misinterpretation.

• You become the go-to person for how things should be modeled and what the data really means.

📌 Functional Engineer Reporting-DataBricks (Pune)
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