Data Operations Manager

Data Operations Manager United Arab Emirates (UAE) Remote Full-time About Department As a data-driven platform, the role of the Data Team at MAGNiTT is to provide quality data around the VC ecosystem in the EVMs, by ensuring accuracy, completeness, reliability, timeliness, and, accessibility.
With a strong focus on data integrity and quality, we aim to leverage our data-driven solutions and support our clients in driving data-based decisions. Our Benefits Hybrid working structure Remote working policies Visa & insurance Leave increment based on tenure Learning platforms Career growth

Job Description
MAGNiTT's competitive edge rests on the accuracy and completeness of its proprietary dataset, and the Data team plays a crucial role in supporting it. Every deal, investor profile, and funding round we track becomes the foundation for the intelligence that GPs, LPs, corporates, and governments across MENA, Southeast Asia, Africa, and other Emerging Venture Markets rely on to source deals, benchmark performance, and report on ecosystem growth. We are looking for a Data Operations Manager to own the integrity of that dataset end to end: leading a small team through the manual research and validation work that keeps records accurate and current, while building the automation, derived metrics, and quality-control processes that let the operation scale without sacrificing precision. If you have experience working in the VC/PE ecosystem, a rigorous, systematic approach to data quality, and experience leading a small team, we should talk about how you might contribute as a Data Operations Manager at MAGNiTT. A good candidate will look to do the following:
- Own MAGNiTT's core dataset end-to-end across all seven entities (Company, Transaction, Investor, Fund, LP, LP Investment, Direct Investments), from collection through cleaning through delivery;
- Define and maintain the schema, data dictionary, taxonomy, methodology, and verification tiers across all seven entities;
- Own data quality end-to-end: validation rules, automated error detection, cleanup workflows, and documentation.
- Set and enforce the write standard for the core dataset;
- Build and maintain data collection tooling, including scrapers and the funding news pipeline, improving coverage, extraction accuracy, and matching to existing records.
- Prototype and validate derived fields and algorithms (classification, matching, inferred metrics) with the Data Scientist, delivering production-ready specs, test sets, and accuracy thresholds for engineering to deploy.
- Design and run structured research workflows (news, filings, press releases, investor disclosures), delegating to and reviewing the work of the team;
- Query MAGNiTT's internal databases (SQL) to audit, reconcile, and QA datasets for product, research, and account management use cases;
- Oversee internal reporting and external data deliverables (e.g. Tableau), maintained within the team rather than built personally;
- Work with product and leadership on coverage priorities and new data points, metrics, or products that pull more value from the existing dataset. Job Requirements
- 5+ years in data-focused roles (analysis or operations), with experience managing or leading a small team;
- Advanced SQL: complex joins, window functions, reconciliation across tables, and awareness of query performance.
- Working proficiency in Python for data work, such as scraping, parsing, cleaning, and prototyping.
- Hands-on experience building or maintaining data collection pipelines or scrapers.
- Experience with entity resolution or record matching (deduplicating companies, people, or organizations across sources).
- Project management experience running data collection and cleanup efforts;
- Experience overseeing reporting/BI deliverables (preference given to Tableau), even if not hands-on building them personally;
- Detail-oriented, with a systematic approach to catching and preventing data errors;
- Product-minded: able to identify coverage gaps and translate them into a prioritized roadmap. Good to have/be
- Experience using LLMs for information extraction or classification, including evaluating their accuracy.
- Experience with data licensing, data partnerships, or due diligence processes.
- Familiarity with the venture capital or private capital ecosystem.
- Experience with Tableau or similar BI tools. Interview Process First Interview A 30-minute video call with the hiring manager, where you can describe your background and highlight your strengths. This is a great opportunity to ask about the specifics of the position. Technical Assignment In this stage you'll be given an assignment to work on, to present and discuss it with the hiring manager. Each assignment has been specifically designed for the respective position, and aims to assess your technical skills and thinking process. Culture Interview Our culture is a big part of MAGNiTT. With this in mind, our