Data Engineer, Employee Technology Support
BloombergAbout the role
Description & Requirements
What's the role?
As an integral member of the Employee Technology Support (ETS) team, you will go beyond collecting and reporting data to build the data foundation that improves support operations through engineering, automation, and scalable analytics. You will design the pipelines, data models, and reporting platforms that turn raw operational data into trusted, decision-ready information across the organization.
We are looking for a Data Engineer who combines strong engineering fundamentals with business acumen and excellent communication skills. You will partner closely with Employee Technology Support, Engineering, Product, and business stakeholders to understand operational challenges and translate them into robust, scalable data solutions.
You will design and maintain data models, ETL pipelines, and business intelligence applications that support critical operational datasets. This includes building automated data flows and reporting, developing intuitive dashboards, improving data quality, modernizing legacy workflows, and creating monitoring frameworks that ensure data accuracy, consistency, and reliability.
Your work will enable analysis of complex support datasets, surfacing trends and root causes, supporting key performance indicators, and delivering insights that improve operational efficiency, employee experience, and strategic decision making.
You will be encouraged to work independently, own solutions end to end, understand the broader business context, and collaborate across multiple teams to deliver high-impact data products. You simplify complex problems, build scalable solutions, and clearly communicate the business impact of your work. In this role you will take ownership of the key data platforms, pipelines, reporting frameworks, and operational datasets that support Employee Technology Support globally.
We'll trust you to
- Partner with Employee Technology Support teams to understand operational challenges and design scalable data solutions.
- Build and maintain robust data models, ETL pipelines, and automated data flows using SQL, Python, and Qlik Sense.
- Develop insightful dashboards, KPIs, and operational metrics that measure service performance, workload, employee experience, and operational efficiency.
- Architect and optimize the data layer that powers operational reporting, ensuring it is performant, well-documented, and reusable.
- Perform data profiling, trend analysis, and root cause investigation to identify opportunities for process improvement.
- Design and implement automated data validation and monitoring to improve data quality, consistency, and reliability.
- Modernize legacy reporting and analytical workflows through automation and scalable solution design.
- Translate business requirements into intuitive visualizations and actionable insights for technical
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