Data Analyst

Data Analyst

Experience: 1–2 Years

About Setu

Setu is reimagining financial products for the next billion users.

Millions of Indians still cannot access insurance, credit, investments, and other financial products through the formal channels many of us take for granted. Setu is solving this by reducing the cost of financial-product distribution.

We break products such as loans, insurance, investments, payments, and account aggregation into fundamental building blocks and offer them as APIs to businesses. This enables companies to build highly customised fintech experiences for their customers and launch products in days instead of months.

Setu is an acquired entity of Pine Labs , one of the fastest-growing fintech companies in India, with a rapidly expanding presence across Asia, the UAE, and the US.

At Pine Labs, we believe in “Every Day is Game Day” — bringing our best selves to work every day to build products that enrich the world through digital commerce and financial services.

About the Role

Setu's Data team works across lending, collections, payments, and account aggregation . We turn transaction data into decisions — for our products as well as for the banks, lenders, and businesses that consume Setu APIs.

As a Data Analyst , you will own the numbers that product, business, and leadership teams rely on every day — and the analysis behind the decisions they make.

You will work with real production data at scale , across multiple products, with direct exposure to how our products, partners, and customers work.

This role is ideal for someone who enjoys going beyond reporting — someone who wants to understand why a number changed, what it means for the business, and what should happen next.

What You'll Do

Product Analytics & Business Thinking

Start with the business question and understand the product, customer journey, and business model before analysing the data.

Translate ambiguous business goals into measurable metrics such as adoption, conversion, success rate, turnaround time, and drop-offs .

Define metrics clearly, align on definitions and edge cases, and ensure consistency across teams.

Turn analysis into actionable recommendations with a clear “so what?”

Analyse user and API journeys end-to-end to identify drop-offs, opportunities, and changes in behaviour.

Dashboards & Reporting

Build and own dashboards used by product, business, and leadership teams.

Analyse funnels, cohorts, segments, partners, merchants, and time-based trends .

Build reusable datasets and documented metric definitions to enable self-serve analytics.

Support A/B tests and pre/post analyses , including guardrail metrics.

Own recurring business reviews — from the numbers and commentary to follow-ups.

SQL, Python & Data Quality

Write SQL every day using joins, aggregations, CTEs, subqueries, and window functions against large transactional datasets.

Investigate slow queries and understand issues related to scan volume, joins, partitions, and data modelling.

Build intermediate tables and views that make downstream analysis faster and more consistent.

Use Python, pandas, and Jupyter for data cleaning, transformation, and analysis.

Validate data before publishing: check row counts, nulls, duplicates, date boundaries, unit mismatches, and source-system reconciliation .

Investigate data-quality issues to identify root causes and work with engineering teams to fix them.

Automate recurring data-quality checks and alerts.

Familiarity with dbt, Jinja-templated SQL, testing, and documentation is a plus.

MLOps Support

Support the data science team with operational data requirements.

Prepare training and evaluation datasets.

Connect outcome labels to historical model predictions.

Contribute to basic monitoring of live model performance and drift.

AI-Native Analytics

Use AI tools and coding assistants to write SQL, explore unfamiliar schemas, analyse data, document work, and automate repetitive tasks.

Understand where AI-generated outputs can be unreliable and verify queries, analysis, and conclusions before using them .

Build lightweight tools, scripts, prompts, or automations that make recurring analytical workflows more efficient.

Ownership & Communication

Proactively identify problems worth solving rather than waiting for tasks to be assigned.

Take ownership of analytical problems from question → analysis → insight → action .

Communicate findings clearly to non-technical stakeholders.

Treat the numbers and dashboards you publish as your product.

Maintain clear documentation of code, metric definitions, assumptions, and methodology.

What We're Looking For

1–2 years of experience as a Data Analyst or in a similar data-focused role.

Bachelor's or Master's degree in Engineering or an equivalent discipline.

Strong SQL is a hard requirement. You should be comfortable with:

Joins

Group By and aggregations

Subqueries

CTEs

Window functions

Working with unfamiliar schemas

Working knowledge of Python, pandas, and Jupyter .

Strong Excel or Google Sheets skills, including pivot tables and advanced analysis.

Experience building dashboards using any BI tool such as Metabase, Superset, Looker, Power BI, Tableau , or similar.

Regular use of AI tools in your analytical or coding workflow.

Strong product thinking — you understand what decision a metric is intended to support.

High attention to data accuracy and correctness .

Working knowledge of basic statistics, including distributions, percentiles, sampling, and statistical significance .

Comfortable working with ambiguity and owning problems end-to-end.

Strong written and verbal communication skills.

Good to Have

Familiarity with Git .

Exposure to how ML models are evaluated and monitored in production .

Curiosity or experience in fintech , particularly lending, payments, collections, or account aggregation.

Familiarity with dbt .

How We Interview

Our interview process focuses on three areas:

1. Technical Skills

SQL

Python

Real-world data problems

2. Problem Solving & Product Sense

Scoping ambiguous questions

Choosing the right metrics

Analysing funnels and cohorts

Reasoning through trade-offs

Converting analysis into actionable insights

3. How You Work

Ownership

Communication

Use of AI tools

Data quality mindset

Ability to work independently

Why Setu?

At Setu, we want you to work on problems that have real-world impact.

You'll get the opportunity to:

Work closely with the founding team that has built and scaled public infrastructure such as UPI, GST, and Aadhaar .

Work with real production data across fintech products at scale.

Solve challenging problems across lending, payments, collections, and account aggregation.

Learn through a fully stocked library and unlimited book budget .

Attend conferences and industry events.

Participate in learning sessions with internal and external experts.

Use our learning and development allowance for courses, certifications, subscriptions, and more.

Benefits

Comprehensive health insurance for you and your family.

Personal accident and term life insurance.

Access to mental health counsellors.

Learning and development allowance.

A beautiful office with natural light and solid-wood interiors.

Great coffee.

Our Culture

Our culture code, How We Move , defines the behaviours we expect from everyone at Setu.

Take the shot — Decide fast and deliver right.

Sign your work like an artist — Master what you do and take pride in it.

Be the sherpa — Lead your crew on every expedition.

Be the CEO of what you do — Own it and make things happen.

Care with tough love — Empower others through trust, respect, and openness.

Own tomorrow — Innovate for the customer and beyond.

If you want to work on challenging data problems, build products that power financial services, and use data to drive real business decisions, we'd love to hear from you.

Seniority level: Associate

Employment type: Full-time

Job function: Information Technology

Industries: Financial Services and Data Infrastructure and Analytics