Lead Analyst, Data Operations

T-Mobile USA; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

T-Mobile USA is seeking an experienced senior data operations leader to design and run enterprise-wide data processes that ensure reliable, accurate data for advertising decisions. You will own data integrations, pipeline automation, governance, and SLAs across on-premises and multiple cloud environments.

You will lead efforts to automate workflows, improve data quality, and mentor analysts while partnering with Engineering, Platform, and vendor teams to scale data-driven decisions in a

Qualifications

• Bachelor's degree in a quantitative field or related discipline.
• Advanced degree plus related work experience preferred.
• Experience translating complex questions into data strategy.

Responsibilities

• Own and enhance enterprise data ops frameworks, SLAs, and escalation across multi-cloud environments.
• Serve as senior authority for data quality, integration, and pipeline issues; collaborate across teams.
• Lead automation migration to self-serve data refresh, reporting, and dashboards.
• Manage data partner and vendor relationships, ensuring SLA and contract terms compliance.
• Drive data governance standards and international data expansion efforts.
• Promote automation and AI tooling; mentor junior analysts without formal supervision.

Education

Bachelor's Degree

Advanced degree

Tools

Snowflake

dbt

Airflow

Power BI

Tableau

SQL

Job description

At T-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That's how we're UNSTOPPABLE for our employees!

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.

Education and Work Experience

• Bachelor's Degree Bachelor's degree plus 3 years of related work experience OR advanced degree with 1 year of related work experience OR combination of education and experience deemed equivalent (Required)
• 7-10 years Experience in data identification, querying, cleaning, wrangling, quality assessment, and management across relational and non relational databases in big data environments, both on-prem and cloud such as Azure, AWS, and Google Cloud. (Required)
• 7-10 years Experience translating complex business questions into data strategy and applying advanced statistical and analytical techniques to drive enterprise-level decisions (Required)
• 7-10 years Experience communicating technical concepts to executive, technical, and lay audiences, including data-driven presentations to senior stakeholders (Required)
• 7-10 years Experience working with relational databases using SQL (Required)
• 7-10 years Experience with data visualization tools such as Power BI, Tableau etc. (Required)
• 4-7 years Experience with big data architecture and pipeline, such as Hadoop, Hive, Spark, and Kafka (Required)
• 4-7 years Experience with enterprise data integration, ETL/ELT automation, and cloud-based data platforms such as Snowflake, dbt, or Airflow (Required)
• 3 - 5 years Experience managing or coordinating vendor and data partner relationships, including SLA and contract performance oversight (Required)
• 3 - 5 years Experience with API-driven data integration, including REST APIs and GraphQL (Preferred)
• 2-4 years Experience coaching or mentoring junior analysts or team members on data analysis and quality best practices (Required)