Data and Analytics Manager
First seen by Alion on Oct 12, 2026.
Data and Analytics Manager - JOFL
Role snapshot
About JOFL
JSW One Finance Limited is an NBFC financing the MSME ecosystem. We are building a technology and data led lending platform across channel finance, vendor finance, loan against property and business loans.
Our core lending systems (LOS, LMS, CRM and Collections) push data into BigQuery on Google Cloud, which powers reporting in Looker Studio. As we scale, data becomes a core input to origination, underwriting, portfolio monitoring, collections, finance and management decisions.
About the role
You will turn business questions into reports people trust, and fix the data underneath so they stay trustworthy. Your first job is reliable reporting: trace the right tables across LOS, LMS and Collections, resolve conflicts, and ship dashboards that Risk, Collections, Finance and Ops rely on.
Your second job is the foundation that makes this repeatable: reusable data models, a single data dictionary, data quality checks and audit ready lineage. Once that is in place, you will extend it into lending analytics such as cohorts, early warning indicators and portfolio segmentation.
Field Detail
Company: JSW One Finance Limited (JOFL)
Function: Data & Analytics
Location: Mumbai (on site)
Experience: 6 - 8 years
Education: Engineering / Statistics / Computer Science / Data Analytics / MBA or equivalent
This is a hands on role. You will write SQL and inspect schemas yourself; this is not a hand off to engineering role.
What you will do
1. Reporting and MIS:
- Convert stakeholder requests from Risk, Collections, Finance and Ops into clear, testable reporting specs with acceptance criteria
- Build and maintain Looker Studio dashboards and recurring MIS across sales, origination, underwriting, disbursement, portfolio / AUM, collections, asset quality and finance
- Replace spreadsheet driven reporting with automated dashboards
- Keep metrics consistent across dashboards, management reviews and Board reporting
- Own and prioritise the reporting backlog; communicate trade offs clearly
2. Data lineage and conflict resolution:
- Trace data lineage across LOS, LMS, CRM and Collections tables to find the correct source of truth for each metric
- Resolve duplicate records, mismatched keys and cross system timing lags before they reach a dashboard
- Fix recurring conflicts at the source with data engineering, not with patches in the report layer
3. Data foundation on GCP:
- Work hands on in BigQuery: write SQL, inspect schemas, manage partitioning and query cost
- Define data models, data marts and reusable datasets for each business function
- Partner with data engineering on pipelines, ETL / ELT and schema changes as source systems are integrated
- Identify and automate manual data extraction and reporting work
4. Data governance and controls:
- Own a single data dictionary and business glossary so metrics mean the same thing everywhere
- Define ownership of critical data elements across business and technology teams
- Set up data quality checks for completeness, accuracy, consistency, timeliness and uniqueness, with a process to monitor and close issues
- Maintain lineage, documentation and evidence for internal audit, statutory audit and RBI requirements
- Work with IT and Information Security on access controls for customer, credit and financial data
- Coordinate data readiness for BCP / DR drills: validate data availability, pipeline recovery and reporting continuity, and track gaps to closure
5. Lending analytics:
- Build datasets for loan and customer level performance: funnel, approval rates, TAT, disbursement, DPD / PAR, collection efficiency and credit cost
- Build portfolio segmentation and cohort analysis
- Identify early warning indicators with Credit, Risk and Collections teams
- Lay the data groundwork for a customer 360 view and future risk, propensity and collections models built with data science partners
6. Stakeholder management:
- Act as the bridge between Business, Risk, Credit, Operations, Finance, Compliance and Technology
- Push back on vague asks and turn them into specs that can be tested
- Present insights to senior management and drive adoption of dashboards across functions
- Manage external data and technology vendors where required
Must haves
- 6 - 8 years in analytics, BI, data management or data product roles
- Strong hands on SQL: joins, CTEs, window functions
- Hands on BigQuery and GCP experience, including partitioning and cost basics
- Proven dashboard building experience in Looker Studio, Looker, Power BI or Tableau
- A track record of resolving data discrepancies across multiple source systems
- Experience defining data models and working with engineering on pipelines and ETL / ELT
- Working understanding of data governance, data quality and data security
- Sharp stakeholder communication: you push back on vague asks and convert them into testable specs
Good to have
- NBFC, banking, fintech or lending experience, especially with LOS, LMS, CRM or collections systems
- Fluency in lending metrics: DPD, PAR, disbursement funnel, collection efficiency, ECL / credit cost
- dbt or a similar transformation layer
- Python for data validation; exposure to statistical modelling or working with data science teams
- Exposure to RBI and audit requirements for data and technology in financial services
- Participation in BCP / DR drills for data platforms
Success criteria
By month 6:
- Stakeholder approved data dictionary for core lending metrics
- Data quality checks live on all critical data elements, with issue tracking
- Report turnaround measured and process driven, with a clear intake and backlog
By month 12:
- Board and management MIS run from automated dashboards, not spreadsheets
- At least one cross system data conflict permanently fixed at source
- Cohort and early warning analytics in regular use by Risk and Collections
- Lineage documented for every metric on management dashboards
- Data evidence for audit and one DR drill delivered without escalations
Skills
Data Analytics, Business Intelligence, Data Management, Data Governance, SQL, Data Quality, Analytics