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Day 134
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Hyderabad, Telangana, India
Open 4 months, first seen by us 134 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full timeFrom the employer’s posting
Reconcile dbt borrowing base outputs against counterparty reports
Investigate and document root causes of variances
Resolve straightforward data issues independently and escalate complex ones
Monitor dbt pipeline runs and triage errors from logs and test failures
Build and maintain reconciliation checklists and validation procedures per facility
Compare outputs from dbt borrowing base pipelines against counterparty reports
Investigate and document the root cause of variances, distinguishing between data quality issues, pipeline logic errors, and legitimate counterparty differences
Resolve straightforward data issues independently, including minor data corrections and known edge cases
Triage pipeline errors by reviewing logs and test failures, providing initial diagnosis before escalation
Develop and maintain reconciliation checklists and validation procedures for each borrowing base facility
Open 4 monthsWe first saw this posting 134 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
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Day 134
Up longer than most. Similar jobs usually come down between day 3 and day 46.
From 404 similar postings that came down. Coming down is not always a hire.
6 open jobs
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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 Requirements What 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 Nice to Have: 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
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New Data Operations postings at the employers we follow are up 203% over the last 6 weeks compared with the 6 before.
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We file this job under Data Operations, by the work the posting describes.
Data operations staff do the day-to-day work of getting data processed correctly: loading and checking files, moving records between systems, fixing errors and following set procedures.
Expect Excel, some SQL, close attention to detail, and knowledge of the particular systems the company runs.
Data governance sets the rules for the data; data operations does the hands-on work of processing it and keeping it moving.
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Hyderabad, Telangana, India
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