Senior Analyst - Data Analytics

Darwinbox Digital Solutions Pvt. Ltd.; India; On-site; INR 2,600,000 - 3,800,000; Full time

Job summary

Darwinbox Digital Solutions Pvt. Ltd. in India is seeking a Consulting Data Architect / Data Modeler to lead enterprise analytics data modeling, BigQuery structures, and Power BI semantic models.

You will define model grain, facts, dimensions, and data contracts, delivering governance and scalable data architectures. You will partner with Data Office and BI developers, own complex modeling deliverables end-to-end, and ensure data quality, lineage, and secure access across analytics products.

Qualifications

• Bachelor's degree in Information Systems or related field.
• 7+ years of experience in data architecture, data modeling, or analytics engineering.
• Advanced SQL experience with cloud analytics platforms (BigQuery, etc.).
• Experience designing enterprise data models for analytics and BI.

Responsibilities

• Own enterprise data architecture and modeling deliverables from requirements through design, validation, release, and maintenance.
• Define model grain, facts, dimensions, keys, relationships, and aggregation strategies.
• Design reusable data structures and datasets for multiple analytics products and consumption patterns.
• Partner with Data Office to govern and maintain analytical data models and data contracts.
• Provide guidance on when to implement calculations in BigQuery vs. semantic models.

Skills

Data modeling

Data architecture

SQL

BigQuery

Power BI

Semantic models

DAX

Governance

Cloud platforms

Education

Bachelor's degree

Tools

Power BI

Job description

The Consulting Data Architect / Data Modeler serves as an enterprise technical authority for analytical data architecture, dimensional modeling, metric design, and the integration between curated BigQuery data structures and shared Power BI semantic models. Operating as part of an offshore analytics hub, this role partners with the Data Office to design, govern, and continuously improve enterprise analytical data models that support reporting, analytics, and data-product consumption.

The role partners closely with the Consulting BI Developer to ensure that SQL structures, model grain, relationships, dimensions, measures, security attributes, and refresh strategies are designed as an integrated solution. The Consulting Data Architect / Data Modeler determines how data and metric logic should be represented across BigQuery and the semantic layer and ensures that governed BigQuery datasets remain usable when direct SQL access is more appropriate than semantic-model consumption.

Responsibilities:

Consulting-Level Ownership &Enterprise Delivery Leadership

• Own complex or enterprise-impacting data architecture and modeling deliverables from requirements and source assessment through design, validation, release, maintenance, and retirement.
• Identify model risks, source limitations, metric inconsistencies, architectural dependencies, data-quality concerns, and scalability issues and recommend practical resolution approaches.
• Provide architectural direction, technical review, mentoring, and escalation support without direct people-management responsibility.
• Partner with the Data Office to design, govern, and maintain conceptual, logical, dimensional, and physical analytical data models.
• Define model grain, facts, dimensions, hierarchies, keys, relationships, history requirements, conformed dimensions, reference data, and aggregation strategies.
• Design reusable curated datasets and data structures that support multiple enterprise analytics products, business functions, and consumption patterns.
• Reduce duplicated datasets, inconsistent structures, conflicting definitions, and fragmented transformation logic through governed enterprise modeling patterns.

Measure, Metric & Business Definition Governance

• Own the technical definition and model representation of enterprise metrics and measures in partnership with Data Stewards, subject matter experts, the Data Office, and the Consulting BI Developer.
• Define metric grain, population, filters, dimensions, aggregation rules, effective dating, calculation logic, exclusions, and appropriate use.
• Determine whether calculations and business rules should be implemented in BigQuery, the Power BI semantic model, or another approved layer based on consistency, performance, reuse, and maintainability.
• Partner with the Consulting BI Developer on DAX implementation, semantic-model behavior, calculation performance, and presentation of governed measures.
• Design and review BigQuery tables, views, transformations, and curated analytical structures in service of enterprise semantic models and analytical use cases.
• Define clear data contracts between curated BigQuery structures and Power BI semantic models, including grain, keys, relationships, quality expectations, refresh requirements, and security attributes.
• Partner with the Consulting BI Developer to resolve issues involving SQL performance, query folding, aggregations, partitions, relationships, refresh behavior, and semantic-model consumption.
• Provide governed BigQuery datasets, documented SQL patterns, and clear usage guidance for advanced, detailed, or high-volume use cases that are not appropriately served by the shared semantic model.

Data Quality, Metadata, Lineage & Access Design

• Define validation rules and quality controls for completeness, accuracy, consistency, timeliness, uniqueness, referential integrity, and fitness for use.
• Collaborate with the Consulting BI Developer to ensure identity, hierarchy, entitlement, and classification attributes support appropriate row-level security, object-level security, and dynamic access patterns.
• Investigate complex data discrepancies and model defects, identify root causes and downstream effects, and implement or coordinate durable corrective actions.

Architecture Standards, Review & Technical Enablement

• Establish and reinforce standards for dimensional modeling, naming, data types, keys, effective dating, metric logic, documentation, lineage, testing, and model review.
• Review high-impact data models, SQL designs, mappings, measures, and data contracts for accuracy, scalability, consistency, maintainability, governance alignment, and semantic-model readiness.
• Apply established practices for version control, peer review, testing, deployment, release management, documentation, security, privacy, and data governance.
• Use approved AI-enabled tools to accelerate model documentation, SQL drafting, mapping analysis, metadata development, lineage summaries, test creation, and design evaluation.
• Independently validate AI-assisted SQL, model recommendations, mappings, definitions, and documentation against governed sources and approved requirements.

Education & Experience:

• Bachelor’s degree in Information Systems, Computer Science, Data Analytics, Engineering, Statistics, Mathematics, Business, or a related field; an equivalent combination of education and experience may be considered.
• 7+ years of progressively responsible experience in data architecture, data modeling, analytics engineering, database design, business intelligence, or a related discipline.
• Advanced experience designing conceptual, logical, dimensional, and physical data models for enterprise analytics environments.
• Advanced SQL experience working with cloud-based analytical platforms, distributed databases, or large and complex enterprise datasets.
• Experience with BigQuery or a comparable cloud analytical database, including table and view design, performance considerations, and curated analytical structures.
• Experience defining and governing metrics, business rules, calculation logic, source-to-target mappings, data contracts, metadata, and lineage.
• Experience designing data structures that support Power BI semantic models or comparable enterprise semantic layers.

Nice to Have Skills

• Experience with Google Cloud Platform, BigQuery optimization, Dataform, dbt, or comparable cloud transformation frameworks.
• Experience with Power BI semantic models, DAX, Tabular Editor, XMLA endpoints, or semantic-model metadata and diagnostic tools.
• Experience with data catalog, lineage, metadata-management, or data-modeling tools.
• Experience with master data, reference data, data products, data mesh, ontologies, or metadata-driven analytics.
• Experience with Git, automated testing, release management, continuous integration and deployment practices, Jira, Azure DevOps, Confluence, or comparable delivery tools.
• Experience supporting globally distributed teams and working across time zones.
• Experience in healthcare, shared services, global capability centers, or another highly regulated enterprise environment.

Licenses, Certifications & Training:

• Preferred: Formal training in dimensional modeling, cloud data architecture, metadata management, data quality, privacy, security, or responsible AI.

Knowledge, Skills, Abilities, Behaviors:

• Advanced knowledge of enterprise data architecture, dimensional modeling, relational structures, analytical databases, semantic layers, and metric design.
• Ability to translate business concepts and analytical requirements into scalable, understandable, and maintainable data models.
• Ability to determine whether data structures, calculations, transformations, quality controls, and access rules should be implemented in BigQuery, the semantic layer, or another approved layer.
• Ability to evaluate models and technical designs for grain, relationships, accuracy, consistency, performance, scalability, usability, lineage, security, and maintainability.
• Ability to identify inconsistencies across source systems, SQL structures, business definitions, metric logic, and downstream analytics products.
• Ability to communicate complex data structures, definitions, risks, tradeoffs, and recommendations clearly to technical and nontechnical audiences.
• Ability to collaborate effectively with the Data Office, Consulting BI Developer, data engineers, analysts, Data Stewards, governance teams, and business subject matter experts.
• Ability to provide constructive architectural guidance, technical review, mentoring, and enablement without direct people-management authority.
• Ability to protect confidential information and consistently apply privacy, security, governance, access-control, and responsible-AI requirements.