Data Analyst

We are seeking a detail-oriented Data Analyst with Azure storage experience to support OCR processing and the management of large document and text datasets used for AI model training.

This role focuses on monitoring existing OCR pipelines, reviewing generated data, maintaining accurate metadata and inventories, and managing Azure uploads and downloads. The analyst will work closely with engineers and applied scientists to identify issues, coordinate reprocessing, and ensure datasets are complete, organized, and ready for downstream use.

Key Responsibilities

OCR Pipeline Monitoring

* Operate existing OCR workflows using established tools and procedures.

* Monitor processing queues, job status, completion rates, and failed or stalled batches.

* Review logs and processing reports to identify exceptions; perform documented recovery steps and escalate technical issues to engineers.

* Maintain batch tracking records and coordinate reruns, priorities, and outstanding processing tasks.

Data Quality and Review

* Review OCR outputs against source documents using sampling and established quality criteria.

* Identify missing pages, empty outputs, duplicate records, unreadable text, formatting problems, and incomplete content.

* Run existing cleaning and validation tools; review results and flag questionable changes for further investigation.

* Maintain issue logs, categorize recurring errors, and track corrections through resolution.

* Summarize quality trends and recommend improvements to review procedures and data handling.

Dataset and Metadata Management

* Maintain inventories linking source documents, OCR outputs, cleaned data, and dataset versions.

* Validate metadata such as document identifiers, titles, subjects, languages, page counts, and processing status.

* Reconcile batch manifests and tracking records against actual files to identify missing, mismatched, or duplicate items.

* Organize generated data using agreed folder structures, naming conventions, and versioning standards.

* Prepare dataset delivery packages with manifests, quality summaries, and documented exceptions.

Azure Data Operations

* Manage uploads, downloads, and transfers of source files and generated datasets in Azure storage.

* Verify transfer completion, file counts, and integrity using established tools and reports.

* Investigate routine transfer failures, incorrect paths, and access issues; coordinate with administrators when needed.

* Follow approved access, retention, and data-sharing procedures; maintain records of dataset deliveries and changes.

Reporting and Coordination

* Maintain operational trackers and dashboards covering processing progress, backlog, quality, and dataset readiness.

* Provide concise updates on completed batches, unresolved issues, and delivery risks.

* Maintain standard operating procedures and support handoffs between data operations, engineering, and model-training teams.

Required Qualifications

Experience in data analysis, data operations, data quality assurance, or dataset management, 2+ years of hands-on experience.

* Hands-on experience with cloud storage, including file organization and uploads/downloads.

* Experience checking data completeness, accuracy, consistency, and metadata quality.

* Ability to use existing dashboards, processing tools, and logs to monitor workflows and investigate exceptions.

* Familiarity with common document and data formats, including PDF, text, CSV, and JSON.

* Strong attention to detail, documentation skills, and clear communication.

* Ability to manage repetitive validation tasks consistently while prioritizing exceptions and following issues through resolution.

Preferred Qualifications

* Experience with AI training-data operations.

* Familiarity with Azure Storage Explorer, AzCopy, or comparable file-transfer tools.

* Basic SQL skills for querying, validation, and reconciliation.

* Basic Python knowledge to run existing scripts or make small adjustments; advanced coding is not required.

* Experience with Power BI or similar operational reporting tools.

* Familiarity with dataset versioning, source traceability, access controls, and human-review workflows.