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The Role
Validate dbt borrowing base pipeline outputs against counterparty reports, investigate discrepancies, monitor pipeline runs, document root causes, resolve straightforward data issues, and escalate complex problems to Analytics Engineering. The role also maintains reconciliation procedures, communicates findings to stakeholders and counterparties, and may contribute minor dbt model fixes. It offers a path to lead a team of 3–5 analysts.
Summary Generated by Built In
As a Data Operations, you will serve as a critical quality control function within our data team. Your primary focus will be validating the outputs of our dbt-based borrowing base pipelines against counterparty reports, identifying discrepancies, and determining causes. You will work closely with our Analytics Engineering team to investigate and resolve data issues, ranging from straightforward fixes you handle independently to more complex problems requiring escalation with clear documentation.
This role is foundational to building out our India-based data operations function. While you will start as a senior individual contributor, there is a clear path to growing and leading a team of 3-5 analysts as we scale.
Responsibilities:
Reconciliation & Validation- Compare outputs from dbt borrowing base pipelines against counterparty reports to identify discrepancies in loan-level data, collateral values, and portfolio metrics
- Investigate and document the root cause of variances, distinguishing between data quality issues, pipeline logic errors, and legitimate counterparty differences
- Develop and maintain reconciliation checklists and validation procedures for each borrowing base facility
Issue Resolution
- Resolve straightforward data issues independently, including minor data corrections and known edge cases
- For complex issues, prepare detailed documentation of findings and escalate to Analytics Engineers with clear context and recommendations
- Over time, contribute minor fixes and pull requests to dbt models as you develop deeper pipeline familiarity
Pipeline Monitoring
- Monitor dbt pipeline runs and flag failures or anomalies to the appropriate team members
- Triage pipeline errors by reviewing logs and test failures, providing initial diagnosis before escalation
Documentation & Communication
- Maintain clear records of reconciliation results, discrepancy resolutions, and recurring issues
- Communicate findings to Analytics Engineers, Operations, and other stakeholders in a clear, structured manner
- Coordinate with external counterparties as needed to resolve data-related questions
RequirementsWhat we are looking for:
- 4+ years of experience in data analysis, data quality, reconciliation, or a related role
- Advanced SQL skills with experience querying complex data sets and troubleshooting data issues
- Experience working with financial data sets (lending, FinTech, financial services, or related industries)
- Strong analytical and problem-solving skills with keen attention to detail
- Excellent written and verbal communication skills, with the ability to document findings clearly and explain technical issues to non-technical stakeholders
- Self-directed with the ability to work independently and manage priorities across multiple facilities
- Familiarity with dbt (Data Build Tool) or willingness to learn quickly
- Experience with Snowflake or similar cloud data warehouses
- Exposure to Git workflows and version control basics
- Understanding of borrowing base mechanics, collateral management, or warehouse lending
- Python proficiency (Pandas or similar) for ad-hoc analysis
- Experience in a reconciliation, audit, or data quality assurance function
Skills Required
- 4+ years of experience in data analysis, data quality, reconciliation, or a related role
- Advanced SQL skills, including querying complex datasets and troubleshooting data issues
- Experience working with financial datasets, such as lending, FinTech, or financial services data
- Strong analytical and problem-solving skills with keen attention to detail
- Excellent written and verbal communication skills, including documenting findings and explaining technical issues to non-technical stakeholders
- Ability to work independently, self-direct, and manage priorities across multiple facilities
- Familiarity with dbt or willingness to learn quickly
- Experience with Snowflake or similar cloud data warehouses
- Exposure to Git workflows and version control basics
- Understanding of borrowing base mechanics, collateral management, or warehouse lending
- Python proficiency, including Pandas or similar tools, for ad hoc analysis
- Experience in reconciliation, audit, or data quality assurance
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The Company
What We Do
Fortunize is a Hyderabad-based consulting and technology services company that helps organizations become more agile and competitive. It partners with clients on strategy, marketing, operations, IT, digital transformation, methodology, advanced analytics, and sustainability. Its offerings include software and mobile app development, machine learning and AI, big data analytics, IT infrastructure management, and IT consulting, serving businesses across industries.
