Senior Business Analyst — Data & Client Intelligence | Finkraft.Ai
location: Bengaluru, HSR | Full-time | Level: L4 | Reports To: BU Head

ABOUT FINKRAFT.AI

Finkraft.ai is a B2B SaaS company that enables enterprises to maximise GST claims on travel and expense data. The platform automates invoice collection, validation, and reconciliation across airlines and hotels — helping finance teams at McKinsey, BCG, Capgemini, Abbott, and 1,250+ enterprises improve compliance, visibility, and cash recovery. We've recovered ₹300Cr+ in GST claims and are scaling from $1.5M to $10M ARR in 18 months.

THE OPERATING PHILOSOPHY OF THIS ROLE

AI-first is not a preference here. It is the baseline. Every analysis, every BRD, every QBR deck, every RCA, and every insight you produce starts with AI augmentation. If you are doing something manually that an AI agent could do faster and better, that is a process failure, not a workstyle choice.

This role exists because Finkraft's enterprise clients generate more data, more edge cases, and more expansion signals than any team can process without AI. You are the person who builds the system that processes it.

WHAT YOU WILL OWN IN 90 DAYS

AI-augmented analysis pipeline live: at least 3 recurring workflows (QBR prep, RCA, client health scoring) running through AI tools — output quality higher, turnaround time lower than manual baseline.

Team operating cadence set: weekly 1:1s and team standups running for both data BAs and field BAs, with structured output templates and quality bar defined.

Customisation pipeline tracked: live dashboard showing open customisation requests, delivery status, SLA compliance, and revenue attribution — reviewed weekly with BU Head.

KEY RESPONSIBILITIES

AI-First Analysis and Insight Generation

Default to AI augmentation for every analytical task: data pulls, pattern identification, RCA structuring, QBR narrative drafting, and BRD first drafts

Build and maintain AI-powered workflows using ChatGPT, Claude, Cursor, or equivalent — document every workflow so the team can replicate it, not just you

Use AI agents to surface current insights from existing client data: anomaly detection, ITC trend analysis, reconciliation failure pattern identification, and upsell signal mining

Measure AI leverage monthly: what percentage of team output is AI-augmented vs. manual — target moves up every quarter, not sideways

Team Leadership — Data BAs and Field BAs

Manage and develop the data BA team (Ranjith's team): structured 1:1s, performance reviews, technical upskilling in SQL, dashboard tools, and AI workflows

Manage and develop the field BA team:



output quality reviews, client observation structuring, and ensuring field findings translate into actionable product inputs within 5 business days

Set a clear quality bar for every output type — QBR deck, RCA document, BRD, dashboard — and hold the team to it without doing the work yourself

Run continuous recruitment for the BA bench: own the JD, screen applications, design the interview loop, and manage the onboarding ramp for every new BA hire

Automation BRDs

Author automation BRDs for engineering: reconciliation logic, validation rules, data transformation workflows, and exception handling — specific enough that engineering can build without a PM translation layer

Identify automation opportunities proactively — anywhere the team is doing something manually more than twice a week, evaluate whether it should be a BRD

Maintain a live automation backlog: prioritised, with business justification, effort estimates from engineering, and expected impact on client outcomes

Own automation delivery from BRD to UAT: you wrote it, you verify it works

Dashboard Ownership

Build and maintain dashboards in Tableau, Power BI, or equivalent: client health scores, issue resolution tracking, SLA compliance, customisation pipeline, and QBR trend data

Dashboards are not maintenance tasks — they are decision tools. Every dashboard you build must answer a specific question for a specific audience

Automate dashboard refresh where possible; reduce manual data preparation to zero on recurring dashboards

Present dashboard insights at weekly internal reviews and monthly BU Head reviews — proactive, not reactive

Enterprise Client Management and Push Back

Run QBRs for the BU's enterprise accounts: own the data pack, the trend narrative, and the action items — not just the slides

Manage enterprise client escalations that reach the Senior BA layer: investigate, respond within SLA, and close the loop in writing

Push back on client requests that fall outside scope, roadmap, or delivery capacity — clearly, with evidence, without damaging the relationship

Protect delivery timelines against scope creep: every new client request gets triaged, estimated,



and formally accepted or declined — no informal commitments

Client Upselling Through Documentation

Build and maintain a upsell documentation library: ROI reports by client segment, value realisation summaries, customisation case studies, and expansion proposal templates

Produce value reports for top-25 accounts quarterly: what Finkraft has delivered, in numbers, against what was promised

Identify expansion signals in client data and QBR conversations; document them with specificity and hand off to Sales with context — not just a name and a note

Track upsell pipeline contribution from the BA layer: how many qualified opportunities identified, how many converted, what revenue attributed

Customisation Pipeline Ownership

Own the full customisation pipeline: intake, scoping, prioritisation, delivery tracking, and client communication

Set quarterly customisation targets with the BU Head: number of customisations delivered, average delivery time, SLA compliance rate, and revenue attributed

Report customisation pipeline status weekly to BU Head: open requests, in-development, delivered, and blocked — with clear owners and dates

Identify customisation patterns that signal a product feature: if 5 clients need the same customisation, it belongs on the product roadmap, not the customisation backlog

Requirements

REQUIREMENTS

Must Have

6+ years in business analysis, data analytics, or consulting in B2B SaaS, fintech, or enterprise services

SQL at an advanced level: complex joins, window functions, aggregations, and query optimisation — not just basic selects

Tableau or Power BI: has built dashboards from scratch, not inherited and maintained

AI tools in daily workflow today: ChatGPT, Claude, Cursor, or equivalent — not experimentally, not occasionally, every day

Has managed a team of analysts or BAs: performance conversations, output quality standards, and difficult calls included

Has presented data-backed analysis to CFOs, tax heads, or equivalent senior client stakeholders — and answered hard questions in the room

Has written BRDs or FRDs that engineering shipped from without a PM translation layer

Strong Preference

Background in management consulting (Big 4, strategy houses) or a high-growth SaaS analytics function

Exposure to GST, indirect tax, travel expense management, or fintech reconciliation

Python or scripting skills for ad-hoc analysis and workflow automation

Has run a recruitment process end-to-end: JD to offer, not just participated in interviews

Pay: ₹1,906,401.40 - ₹3,000,029.66 per year

Work Location: In person

📌 Senior Business Analyst (India)
🏢 finkraft.ai
📍 India