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Air

Lead Data Operations & Quality

Pittsburgh, Pennsylvania, United States; Pittsburgh OfficeUnited States
Staff · Data
POSTED 13 APRVERIFIED 57M AGO
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Role
213 days opentrue age
Posted 13 Apr
Pay
$38k–$160kno pay posted
Market p25–p75
$38k–$160k
Pittsburgh · n=7 · 365d to 10 Nov
CompanyAir →
33open roles
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Closed · 30d
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All tracked boards · live 12 Nov · n=23,176
PythonSQL

Company Description

Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.

Job Description

We are seeking an exceptional and experienced Senior Content Operations team member, a role for someone who shares our passion and obsession with quality. You will be a core member of our team dedicated to helping our clients replace time-consuming, manual processes to achieve informed, real-time decisions about government markets, competitors, and agency relationships. The ideal candidate is a hands-on analyst with large dataset experience. You must also possess strong interpersonal skills to work cross-functionally across internal teams and with Air platform Subject Matter Experts (SMEs).

You will support the Content Analyst Manager in overseeing two offshore teams across split shifts, ensuring the delivery of high-quality, fast-turn validation and QA. This role involves assisting with day-to-day team management, workload prioritization, and performance support while maintaining high-confidence accuracy across every data point. You will own feedback loops and process improvements—leveraging technical skills to reduce rework, improve scalability, and strengthen overall execution—while reporting progress, risks, and outcomes directly to the Content Analyst Manager.

This role is a full-time position located out of our office in Pittsburgh, PA.

This role may require up to 10% travel

Scope of Responsibilities

The Senior Team member you will manage and provide leadership for two offshore teams, overseeing daily workflow, assignments, prioritization, throughput, and SLA delivery. This includes consistent coaching (calibrations, feedback, 1:1s/lead check-ins) to maintain morale and accountability across different cultures and hours, as well as escalating risks, resourcing gaps, and performance concerns to the Content Analyst Manager.

The role also involves overseeing manual QA and validation, especially for high-volume checks with quick turnaround. Key responsibilities include triaging requests, prioritizing workloads, meeting SLAs, establishing feedback loops to reduce errors and improve accuracy, and documenting issues and resolutions to enhance future execution. Daily/weekly status updates on volume, trends, and blockers are required.

Further responsibilities encompass maintaining and improving SOPs, QA guidelines, and validation standards; tracking key performance metrics and risks; leading calibration and review routines; and supporting performance coaching and skill development.

Qualifications

  • U.S. Citizenship is required

Required Skills

  • 3+ years of experience in content operations, data operations, research operations, data QA/validation, or similar roles supporting structured data workflows
  • 1–3+ years experience leading analysts or data QA teams (including offshore and/or shift-based coverage preferred)
  • Proven ability to manage high-volume validation work with tight SLAs, competing priorities, and frequent context switching
  • SQL and Python experience
  • Experience working with structured datasets, logs, and validation outputs to identify root causes and reduce rework
  • Familiarity with data QA concepts (sampling methods, error categorization, trend tracking, and continuous improvement loops).
  • Demonstrated success in coaching teams, improving performance, and maintaining high-quality standards
  • Strong judgment and attention to detail when validating complex relationships and resolving ambiguous cases
  • Excellent written communication — able to document decisions with clear reasoning and auditable source support
Source · AirFirst seen 3 NovOriginal ↗