Senior Data Analytics Engineer

Experity; Bengaluru; Presencial; INR 4 200 000 - 6 200 000; Tempo integral

Resumo da oferta

Experity is seeking a Senior Analytics Engineer in Bengaluru to scale analytics foundations across products and AI experiences. You will own semantic models, trusted metrics, and reusable datasets, enabling AI-ready data products and scalable analytics across teams.

You’ll work with SQL, Python, Snowflake, and dbt to implement production-grade transformations, set up testing and data quality controls, and collaborate with global stakeholders to deliver coherent analytics foundations and reusable

Qualificações

• 5+ years in analytics engineering, data engineering, BI engineering, or related field.
• Bachelor’s degree in a technical/analytical field or equivalent practical experience.
• Advanced SQL with focus on performance and maintainability.
• Python for data processing, automation, testing, or analytics workflows.
• Strong experience with Snowflake or another modern cloud data warehouse.
• Experience with dbt or similar transformation framework.
• Understanding of dimensional modeling and reusable analytical design.
• Experience designing production-quality analytical models, not just consuming datasets.
• Experience with Git, automated testing, CI/CD, and production software practices.
• Ability to turn ambiguous requirements into clear analytical solutions.
• Ability to explain modeling decisions to technical and nontechnical stakeholders.
• Experience working with distributed or cross-functional teams.

Responsabilidades

• Own analytical domains from requirements through implementation, testing, deployment, and production support.
• Design and maintain semantic models representing products, processes, and core entities.
• Define reusable datasets, governed metrics, and centralized business logic.
• Translate ambiguous needs into scalable data products, not one-off solutions.
• Determine data grain, facts, dimensions, conformed entities, and relationships.
• Decide where business logic belongs: transformation vs semantic layer vs consuming app.
• Develop production-quality transformations using SQL, Python, Snowflake, and dbt.
• Establish automated testing, documentation, monitoring, and data-quality controls.
• Design semantic models and metadata to enable AI tools and NLP interfaces to interpret data.
• Collaborate with U.S.-based stakeholders to clarify requirements and tradeoffs.
• Raise unclear requirements, data quality concerns, and architectural risks.
• Partner with data engineers and architects for scalable patterns.
• Lead onboarding for new products and domains in analytics engineering.
• Identify duplicated logic and opportunities for automation or reuse.
• Create reusable patterns across domains and review others’ designs.

Conhecimentos

Advanced SQL

Python

Dimensional modeling

Cross-functional collaboration

Problem solving

Communication skills

Independent work

Formação académica

Bachelor's degree

Ferramentas

Snowflake

dbt

Git

Tableau

Descrição da oferta de emprego

Position Overview

We are looking for a Senior Analytics Engineer to help define and scale analytics across Experity.

This role will own analytical domains and build semantic models, trusted metrics, reusable datasets, and AI-ready data products. The Senior Analytics Engineer will work across analytics, data engineering, architecture, product, and AI teams to translate ambiguous business needs into scalable analytical solutions.

This is not primarily a dashboard development or traditional reporting role. The focus is creating trusted analytical foundations that can be reused across products, teams, applications, and AI experiences.

The ideal candidate combines strong technical execution with analytical judgement. They can independently work through ambiguity, challenge assumptions, make sound modelling decisions, and improve how analytics is delivered.

What You’ll Do

• Own assigned analytical domains from requirements clarification through implementation, testing, deployment, documentation, and production support.
• Design and maintain semantic models representing Experity’s products, business processes, and core business entities.
• Define reusable analytical datasets, governed metrics, and centralized business logic.
• Translate ambiguous business needs into scalable data products rather than one-off analytical solutions.
• Determine appropriate data grain, facts, dimensions, conformed entities, and relationships.
• Make informed decisions about whether business logic belongs in the transformation layer, semantic layer, or consuming application.
• Develop production-quality transformations using SQL, Python, Snowflake, and dbt.
• Establish automated testing, documentation, monitoring, and data-quality controls for owned models.
• Design semantic models, metadata, metric definitions, descriptions, and verified query patterns that enable AI tools and natural-language interfaces to reliably interpret analytical data.
• Use AI-assisted development tools to improve engineering speed, quality, and consistency.
• Partner directly with U.S.-based analytics, engineering, product, and business stakeholders to clarify requirements and identify tradeoffs.
• Proactively raise unclear requirements, data quality concerns, architectural risks, and design decisions.
• Collaborate with data engineers and architects to establish reliable and scalable analytical patterns.
• Lead analytics-engineering onboarding for new products and business domains.
• Identify duplicated logic, inconsistent metric definitions, architectural gaps, and opportunities for automation or reuse.
• Create reusable patterns that can be adopted across domains rather than solving the same problem repeatedly.
• Review analytical designs and code produced by other engineers and contribute to engineering standards and best practices.
• Troubleshoot complex production, performance, and data-quality issues.

What Success Looks Like

• Assigned analytical domains can move from business requirements to production with limited supervision.
• Metrics are trusted and consistent across BI tools, applications, products, and AI experiences.
• Priority domains have reusable, well-documented semantic models.
• Business logic is centralized, testable, version-controlled, and appropriately governed.
• Architectural and data-quality issues are identified before they become downstream reporting problems.
• Analysts spend less time rebuilding foundational logic or reconciling competing metric definitions.
• New products and domains follow reusable patterns rather than starting from scratch.
• Patterns established in one domain are successfully reused across additional domains.
• Junior and mid-level engineers benefit from technical guidance, reviews, and established engineering practices.

Required Qualifications

• 5+ years of experience in analytics engineering, data engineering, business intelligence engineering, advanced data analytics, or a related field.
• Bachelor’s degree in a technical, analytical, or related field, or equivalent practical experience.
• Advanced SQL skills and strong understanding of query performance and maintainability.
• Working proficiency with Python for data processing, automation, testing, or analytics engineering workflows.
• Strong experience with Snowflake or another modern cloud data warehouse.
• Strong experience with dbt or a similar transformation framework.
• Strong understanding of dimensional modeling, data grain, facts, dimensions, conformed dimensions, business entities, and reusable analytical design.
• Demonstrated experience designing production-quality analytical models rather than only consuming existing datasets.
• Experience with Git, automated testing, code review, CI/CD, and production software development practices.
• Ability to independently turn ambiguous requirements into clear analytical solutions.
• Ability to evaluate technical tradeoffs and explain modeling decisions to technical and nontechnical stakeholders.
• Strong problem‑solving, ownership, and communication skills.
• Experience working effectively with distributed or cross‑functional teams.

Preferred Qualifications

• Experience designing semantic layers, metrics layers, or governed analytical data products.
• Experience preparing data, metadata, metric definitions, or semantic models for LLMs, AI agents, or natural‑language analytics.
• Experience with Tableau as a consumer of governed data.
• Experience in healthcare, SaaS, or multi‑product software environments.
• Experience with data lineage, observability, governance, metric certification, and production monitoring.
• Experience supporting internal, client‑facing, or embedded analytics.
• Experience mentoring engineers, reviewing analytical designs, or establishing reusable development patterns.

Why Join Experity?

• Help define the analytical foundation for the next generation of analytics and AI capabilities for urgent care.
• Work with valuable healthcare and operational data across multiple products and business domains.
• Build reusable data products used by employees, clients, applications, and AI systems.
• Influence analytics engineering standards, modeling practices, and architecture.
• Work with modern technologies including Snowflake, dbt, semantic layers, Tableau, and AI‑assisted development tools.
• Help shape and scale a growing analytics engineering capability.

Experity is an equal opportunity employer committed to fostering a diverse and inclusive workplace.

Equal Opportunity Employer

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