Data Analyst – AI Training Data & Azure Operations

Team Red Dog•Redmond, WA

1d•Onsite

About The Position

Team Red Dog is hiring a Data Analyst to support an applied science team developing AI models for Windows and devices. The role focuses on OCR processing, Azure cloud storage, data quality assurance, and large-scale dataset management. The analyst will monitor processing workflows, validate document and text datasets, maintain accurate metadata, and work with engineers and applied scientists to prepare high-quality data for AI model training. The position offers hands-on exposure to AI training-data operations, OCR processing, and Azure cloud technologies while supporting confidential, cutting-edge initiatives. The role is based onsite in Redmond, Washington, five days per week, Monday through Friday, for 40 hours per week. It is a W2 contract through June 30, 2027, with potential to extend up to 18 months total, subject to performance, budget, and client discretion.

Requirements

Bachelor's degree required.
Minimum 2 years of hands-on experience in data analysis, data operations, data quality assurance, or dataset management.
Practical experience managing files and datasets in cloud storage environments, including uploads, downloads, and file organization.
Ability to evaluate data completeness, accuracy, consistency, and metadata quality.
Experience monitoring workflows using existing dashboards, processing tools, logs, and operational reports.
Familiarity with PDF, text, CSV, and JSON file formats.
Strong attention to detail and ability to identify discrepancies, maintain accurate records, and consistently execute repetitive validation tasks.
Strong written and verbal communication skills, including the ability to provide clear status updates and communicate technical issues effectively.
Excellent organizational and documentation practices, with the ability to prioritize exceptions and follow issues through resolution.
Applicants must be authorized to work in the U.S. without sponsorship.
Employment is contingent upon successful completion of a reference and background check.

Nice To Haves

Experience with Microsoft Azure Storage, Azure Storage Explorer, AzCopy, or comparable cloud file-transfer tools.
Familiarity with OCR processing, document extraction, or AI training-data operations.
Basic SQL skills for data querying, validation, and reconciliation.
Basic Python knowledge for running existing scripts or making minor adjustments; advanced programming skills are not required.
Experience with Power BI or similar operational reporting tools.
Familiarity with dataset versioning, source traceability, access controls, and human-review workflows.
Experience in a large enterprise technology environment, particularly Microsoft or another major technology company.
Practical knowledge of Azure cloud storage and large-scale dataset management.

Responsibilities

Monitor OCR processing pipelines, including job status, completion rates, queues, and failed or stalled batches.
Review processing logs and reports, investigate exceptions, perform documented recovery procedures, and escalate technical issues to engineering teams.
Validate OCR-generated outputs against source documents and identify missing pages, duplicate records, formatting errors, incomplete content, and other data quality issues.
Run established data cleaning and validation tools, review results, and document discrepancies requiring investigation.
Maintain dataset inventories, metadata, batch manifests, and version tracking to support completeness and traceability.
Manage uploads, downloads, and transfers of source documents and generated datasets using Azure cloud storage.
Verify file transfer completion, reconcile file counts, investigate routine transfer issues, and maintain organized storage structures.
Prepare datasets for downstream AI model training, including delivery manifests, quality summaries, and documented exceptions.
Maintain operational dashboards and tracking reports covering processing progress, data quality, backlogs, and dataset readiness.
Collaborate with engineers, applied scientists, and data operations teams to resolve issues, coordinate reprocessing, and mitigate delivery risks.
Document standard operating procedures, track recurring issues, and recommend improvements to data handling and quality review processes.
Follow established data security, access control, retention, and confidentiality requirements.