Senior Data Analytics Engineer

Overview

Own analytical domains and build semantic models, trusted metrics, reusable datasets, and AI-ready data products that serve analytics, products, applications and AI experiences across Experity.

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 you'll need

• 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.

Nice to have

• 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.

Details

• Location: Bengaluru Office.
• Partners directly with U.S.-based analytics, engineering, product, and business stakeholders.