Reporting Contractor - Settlement & Reconciliation
paysend
| Company | paysend |
| Category | Data & Analytics |
| Location | — |
| Remote | — |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 23 Jul 2026 |
| Last verified | 5 Aug 2026 |
| Source | Employer ATS (teamtailor) |
Description
About The Role: We're looking for a detail-driven Reporting Contractor to join our Settlement & Reconciliation team and play a key role in how we turn complex, high-volume data into clarity. Working closely with our PDP database and Databricks environment, you'll design and build the reports that our reconciliation specialists rely on every day to catch discrepancies, resolve settlement breaks, and keep our financial operations running smoothly. This is a role for someone who enjoys solving puzzles hidden in large datasets and takes pride in turning messy, complex information into clean, reliable reports that people can actually trust and act on. If you love working with SQL and Python, thrive on precision, and want your work to have a direct, visible impact on how a financial operations team functions, we'd love to hear from you. Note: This is a 6-12-month fixed-term contract position. What You'll Do (Responsibilities): Report Design & Development: Design, build, and maintain reports sourced from the PDP database (integrated with Databricks), tailored to settlement and reconciliation workflows. Translate business requirements and stakeholder instructions into clear accurate, and well-structured report specifications. Ensure reports are consistent in format, logic, and presentation, and easy to interpret by both technical and non-technical stakeholders. Maintain version control and documentation for report logic/queries/notebooks to support auditability. Data Management & Analysis: Query, extract, clean, and validate large volumes of transactional and settlement data from PDP/Databricks and other source systems. Write and optimize SQL queries to pull and aggregate data efficiently at scale. Perform data processing, automation, and recurring reconciliation tasks within the Databricks environment. Identify data discrepancies, anomalies, or breaks within the data and flag them for the reconciliation team. Settlement & Reconciliation Reporting: Build recurring and ad hoc reports covering transaction matching, exceptions, outstanding items, and settlement status Support the reconciliation team by structuring data outputs that highlight mismatches, aging items, and unresolved settlement breaks. Ensure reports align with internal control requirements and support audit/compliance needs. Collaborate with the Settlement & Reconciliation team to refine report logic as data structures or business processes evolve. Automation & Process Improvement: Automate manual, repetitive reporting tasks to increase accuracy and reduce turnaround time. Recommend and implement improvements to reporting pipelines, queries, and Databricks notebooks/workflows. Maintain scalability and performance of reporting solutions as data volume and complexity grow. What You’ll Need To Be Successful In This Role: Bachelor's degree in a relevant field (Computer Science, Information Systems, Finance, Mathematics, or related) - or equivalent practical experience. Strong proficiency in SQL, with demonstrated ability to write and optimize complex queries against large relational and distributed databases. Hands-on experience with Databricks (notebooks, SQL warehouses/clusters, and ideally Spark SQL or PySpark for large-scale data processing). Proficiency in Python for data processing, automation, and report generation, particularly within a Databricks/notebook environment. Direct experience working with PDP or comparable enterprise transactional databases is strongly preferred. Excellent attention to detail and data accuracy, given the sensitivity of settlement data. Ability to interpret business/stakeholder requirements and convert them into functional, well-structured reports without extensive back-and-forth. Experience handling large-scale/distributed datasets and optimizing query/report performance under high data volume. Familiarity with version control (e.