Senior Business Analyst

Finkraft; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

Finkraft, located in Bengaluru, India, is looking for an experienced leader to oversee AI-augmented analytics and manage data BA teams. The successful candidate will leverage advanced SQL and BI tools to enhance insights for enterprise clients. Responsibilities include running QBRs, authoring automation BRDs, and fostering team development.

The role requires 6+ years in business analysis within SaaS or fintech, alongside a proven track record of managing teams and presenting to senior stakeholders.

Qualifications

• 6+ years in business analysis, data analytics, or consulting in B2B SaaS, fintech, or enterprise services.
• Advanced SQL skills with complex queries and optimizations.
• Proficiency in Tableau or Power BI, building dashboards from scratch.
• Regular use of AI tools in daily workflows.
• Experience managing teams of analysts or BAs.

Responsibilities

• Manage AI-augmented analysis and insight generation.
• Lead and develop data BA and field BA teams.
• Author automation BRDs for engineering teams.
• Run QBRs for enterprise accounts and manage escalations.
• Build upsell documentation and track client customization status.

Skills

SQL (advanced proficiency)

Tableau or Power BI

AI tool usage

Team management

Business analysis

Tools

ChatGPT

Claude

Cursor

Job description

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 new 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
  
• 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
  
• 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