Lead Analyst, Data Operations

Location: New York, New York

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Job Overview

This role leads the design, execution, and continuous improvement of enterprise-wide data operations processes that ensure the reliability, quality, and accessibility of data supporting T-Ads business decisions. It serves as the senior technical authority for data integrations, pipeline automation, and governance across on-premises and multi-cloud environments, partnering directly with Engineering, Platform, and external vendor teams to resolve complex, high-impact issues.

The role owns end-to-end data operations frameworks, service-level standards, and escalation processes, and acts as a technical coach and mentor to junior and mid-level data analysts, without formal supervisory authority. Success is measured by the scalability, automation, and reliability of data operations, the strength of vendor and governance relationships, and the role's ability to elevate technical capability across the broader team.

This work directly enables T-Ads to scale data-driven advertising decisions with speed, accuracy, and trust.

Job Responsibilities

Own and continuously enhance enterprise data operations frameworks, service-level standards, and

escalation processes spanning data integration, pipeline automation, billing, file transfer, and governance

across on-premises and multi-cloud environments (Azure, AWS, Google Cloud).

Serve as the senior technical authority and escalation point for complex data quality, integration, and

pipeline issues, building and maintaining infrastructure across tools including Snowflake, AWS Lambda, S3,

and Streamlit, and partnering with Engineering, Platform, and external vendor teams to drive root-cause

resolution.

Lead migration of manually-maintained processes to fully automated, documented systems — eliminating

single points of failure and driving self-service capabilities for data refresh, reporting, and dashboard

processes.

Manage data partner and vendor relationships, ensuring adherence to SLAs, contract terms, and

performance standards for data tools and platforms.

Drive data governance standards and international data expansion efforts across partner integrations.

Increase adoption of automation and AI tooling to reduce manual effort and free up capacity for higher

leverage technical work. Coach and mentor junior and mid-level data analysts on advanced data analysis, quality, and tooling best

practices, elevating technical capability and data literacy across the team, without formal supervisory

authority.

Coach and mentor junior and mid-level data analysts on advanced data analysis, quality, and tooling best practices, elevating technical capability and data literacy across the team, without formal supervisory authority; performs other duties and projects as assigned by business management as needed.

Performs other duties and projects as assigned by business management as needed.