Analytics Engineer

The Opportunity

We are looking for an Analytics Engineer to join our data team and strengthen the foundation of our modern data stack. In this role, you’ll take ownership of critical transformation pipelines, design scalable data models, and ensure that our analytics environment is performant, trustworthy, and future-proof.

As an AE, you’ll bring strong fundamentals in SQL, data modeling, and dbt while also leaning into emerging best practices like semantic layers, medallion architecture, and lakehouse paradigms. You’ll be expected to operate with a high degree of independence, and drive projects from design through production.

What You’ll Do

• Take ownership of large portions of our dbt project (3k+ models, custom macros, tests), ensuring scalability, maintainability, and adherence to best practices.

• Design and implement robust data models using dimensional modeling, incremental strategies, and Slowly Changing Dimensions (SCDs).

• Establish and enforce dbt test coverage, automated quality checks, and CI/CD pipeline standards (GitHub Actions).

• Profile and optimize SQL queries and warehouse performance, with a focus on efficiency and cost reduction.

• Build and refine our semantic layer, ensuring consistent business logic across Looker, Redash, and downstream tools.

• Collaborate with analysts and business partners to define metrics and deliver self-serve data assets.

• Document models, lineage, and transformation logic, making data discoverable and usable across the company.

• Contribute to shaping team standards and playbooks, collaborating with analysts in modeling and transformation best practices.

• Stay ahead of the curve on modern data stack innovations (dbt metrics layer, universal semantic layers, data contracts, observability).

Enabling Self-Serve Analytics & AI

• Design data models for self-service: Build transformations that empower business stakeholders and analysts to confidently explore, slice, and deep dive into data without depending on engineering.

• Semantic layer ownership: Ensure that definitions of metrics and dimensions are consistent, discoverable, and reusable across BI tools and ad hoc queries.

• AI-ready data foundation: Prepare clean, structured, and accessible datasets to support initiatives like conversational analytics, anomaly detection, and AI-driven decision support.

• Partner with data science & ML teams: Provide reliable, well-modeled data pipelines that accelerate experimentation and deployment of AI/ML features.

• Champion usability: Advocate for modeling approaches that balance performance, governance, and accessibility so data can be confidently used by both technical and non-technical stakeholders.

About You

• 3–5 years of experience in analytics engineering roles.

• Advanced SQL skills (query optimization, performance tuning, execution plans).

• Strong proficiency with dbt Core: models, macros, snapshots, sources, custom materializations.

• Solid background in data modeling techniques (Kimball, SCD handling, incremental pipelines).

• Practical experience with semantic layers and BI integration (LookML, dbt metrics, or equivalent).

• Familiarity with Medallion architecture and modern lakehouse approaches.

• Hands-on experience with Redshift and exposure to Databricks (experience with Databricks is a strong plus).

• Proficiency with GitHub and CI/CD pipelines for analytics code.

• Strong fundamentals in data quality, governance, and lineage tracking.

This position requires you to be present in our Bangalore office on Mondays, Wednesdays & Fridays.