Analytics Engineer

Curefit; Bengaluru; On-site; INR 1,400,000 - 2,800,000; Full time

Job summary

Curefit is seeking an Analytics & Data Platform engineer in Bengaluru to own the modeled layer of our data warehouse, maintain datasets and metrics used company-wide, and ensure fast, accurate pipelines.

You'll work at the join of data engineering and analytics, building lakehouse models, ELT pipelines, and self-serve BI layers, coordinating with Growth, Supply, and Member Experience teams. Requires 4–6 years of analytics/data engineering experience and strong SQL/Python skills.

Qualifications

• 4–6 years in analytics engineering, data engineering, or BI engineering.
• Expert SQL with window functions and performance judgment.
• Strong dimensional modelling instincts for facts and dimensions.
• Working Python for pipelines, validation, and automation.
• Bias for ownership: detect and fix data issues in pipelines.
• Experience with lakehouse/warehouse at scale (Databricks, Spark, Snowflake, BigQuery).
• Clear written communication; document models and decisions.

Responsibilities

• Data models and marts: design and maintain dimensional models and datasets.
• The metric layer: define metrics centrally for consistent dashboards.
• Pipelines: build and operate ELT from event streams into the lakehouse.
• Data quality: tests, freshness checks, anomaly detection, reconciliation.
• Self-serve analytics: curate BI layer for analyst self-sufficiency.
• Cost and performance: optimize queries and storage layout.
• Automation: convert recurring toil into scripts and scheduled jobs.

Skills

SQL

Python

Dimensional Modelling

Data Pipelines

Data Quality

ELT

Databricks/Spark

Communication

Tools

Databricks

Spark

Snowflake

BigQuery

Job description

About Cult - Curefit Healthcare Pvt Ltd, founded in 2016, is India’s largest fitness company and wellness platform. Cult, from the house of Curefit, was established with a mission to make fitness easy, fun and accessible to everyone, and has emerged as a community celebrating the joy of fitness. The brand today offers a range of fitness services ranging from group workouts, gyms and personalized fitness solutions, as well as an expansive selection of quality fitness products for the everyday athlete. The company’s vision is to empower everyone towards an active and healthier lifestyle through innovative fitness solutions, enabled by technology.

Job Summary - The Analytics & Data Platform team owns the company's data end to end: the lakehouse, the pipelines, the metric layer, and the analytics that Growth, Supply, and Member Experience teams run on every day. This role sits at the join between data engineering and analytics. You'll own the modelled layer of our warehouse the datasets and metrics the whole company treats as the source of truth and the pipelines that keep them fresh, correct, and fast.

WHAT YOU'LL OWN

• Data models and marts. Design, build, and maintain the dimensional models and certified datasets that power company-level metrics revenue, retention, utilisation, funnel.
• The metric layer. Define metrics once, centrally, and onboard business teams onto them — so every dashboard agrees with every other dashboard.
• Pipelines. Build and operate reliable ELT from event streams and transactional sources into the lakehouse with SLAs, alerting, and backfill discipline.
• Data quality. Tests, freshness checks, anomaly detection, and reconciliation against source systems, so issues surface before a stakeholder finds them.
• Self-serve analytics. Curate the BI layer so analysts and operators answer their own questions instead of filing tickets.
• Cost and performance. Tune queries, partitioning, and storage layout so the platform stays fast as data volume grows.
• Automation. Turn the team's recurring toil — backfills, reconciliations, audits, recurring pulls into scripts and scheduled jobs that run without anyone watching.

WHAT WE'RE LOOKING FOR

• 4–6 years in analytics engineering, data engineering, or BI engineering — ideally at a product startup where you owned datasets people depended on.
• Expert SQL: window functions, query plans, and the judgement to know when a query is wrong even when it runs.
• Strong dimensional modelling instincts — facts, dimensions, slowly changing history, and when to break the rules.
• Working Python for pipeline logic, validation, and automation.
• A scripting and automation mindset. If you've done something by hand twice, your instinct is to script it the third time and to leave the script behind, documented, for the next person.
• Hands‑on experience with a lakehouse or warehouse at scale (Databricks, Spark, Snowflake, BigQuery, or similar).
• A bias for ownership: you notice broken data, trace it to the root, and fix the pipeline — not just the number.
• Clear written communication — you document models and decisions so others can build on them.