Engineering Manager

About the Team The Analytics Platform department builds the distributed infrastructure that powers Bloomberg's core analytics ecosystem, including CalcrtX, BQL, and the evolution of the legacy Calcrt platform. Our systems form the foundation for how Bloomberg applications and clients access structured data, analytics, and real-time content at global scale.

The Anomaly Detection team is responsible for ensuring the quality, reliability, and performance of Bloomberg's data and analytics services. We build the platforms and tooling that help engineering teams detect data quality and performance regressions early in the software development lifecycle, enabling developers to deliver highly reliable analytics at Bloomberg scale.

About the Role Our work directly supports the Calcrt ecosystem, which processes hundreds of billions of data requests every day and serves as the backbone for Bloomberg Terminal and Enterprise products. As Bloomberg continues to modernize its analytics infrastructure, we're building the next generation of automated validation platforms that enable engineering teams to continuously monitor, validate, and improve the quality of the data and analytics they deliver.

Our mission is to provide a scalable, self-service platform for anomaly detection that empowers development teams across Bloomberg to identify issues before they reach production. We combine distributed data processing, intelligent analytics, and developer-focused tooling to simplify onboarding, automate remediation workflows, and continuously improve the health of Bloomberg's analytics ecosystem. Looking ahead, we're exploring AI and machine learning techniques to make anomaly detection, sampling strategies, and issue prioritization smarter and more adaptive.

Tech Stack

Programming Languages: Python, Java
Data Processing: Apache Spark
Messaging: Apache Kafka, Distributed Message Queue Technologies
Databases: PostgreSQL
Containerization: Docker, Kubernetes
CI/CD: Jenkins, SonarQube
Infrastructure: Linux, Distributed Systems, Microservices
AI & Automation: Machine Learning, AI-assisted Development Tools

What's in it for you?

If you're excited about leading engineers while solving large-scale distributed systems challenges, you'll have a home here.

As Team Leader for Anomaly Detection, you'll shape the strategy and technical direction of a platform that helps engineering teams across Bloomberg deliver reliable, high-quality analytics. You'll work with senior engineering leaders to define the roadmap, mentor engineers, and build innovative solutions that improve software quality across Bloomberg.

You'll have the opportunity to:

• Lead the technical evolution of Bloomberg's anomaly detection platform.

• Define architecture for highly scalable distributed systems processing massive volumes of financial data and telemetry.

• Build self-service platforms that improve developer productivity across Bloomberg Engineering.

• Drive adoption of AI and machine learning techniques to enhance anomaly detection and operational automation.

• Influence engineering practices and quality standards across multiple organizations.

• Mentor and grow a high-performing engineering team while remaining technically engaged.

• Partner with senior engineering leadership on strategic, high-impact initiatives.

We'll trust you to

• Lead a team of software engineers through the design, development, and delivery of scalable anomaly detection platforms.

• Define the technical roadmap and architecture for systems that improve data quality and engineering productivity across Bloomberg.

• Drive technical decisions balancing scalability, performance, reliability, and operational simplicity.

• Foster a collaborative engineering culture focused on ownership, innovation, and continuous improvement.

• Mentor engineers through technical guidance, code reviews, architecture discussions, and career development.

• Partner with stakeholders across Analytics Platform and other engineering organizations to prioritize initiatives and deliver shared infrastructure.

• Build strong operational practices through observability, automation, testing, and continuous delivery.

• Champion AI-assisted engineering practices and evaluate opportunities to apply machine learning to anomaly detection and intelligent automation.

• Take ownership of strategic initiatives from conception through production while ensuring successful execution across multiple teams.

You'll need to have

• Significant experience designing and building large-scale distributed systems and data-intensive applications.

• Strong software engineering experience with Python, Java, or similar programming languages.

• Demonstrated experience leading engineering teams or serving as a technical lead on complex, cross-functional projects.

• Deep understanding of distributed systems, software architecture, and scalable platform design.

• Experience building highly reliable, observable, and maintainable production systems.

• Strong communication and stakeholder management skills with the ability to influence across organizations.

• Experience mentoring engineers and fostering technical excellence.

• A degree in Computer Science, Engineering, Mathematics, a related field, or equivalent practical experience.

We'd love to see

• Experience leading platform or infrastructure engineering teams.
• Experience with Apache Spark, Kafka, or other distributed data processing technologies.
• Experience building developer platforms, internal tooling, or self-service infrastructure.
• Knowledge of distributed messaging systems, event-driven architectures, and large-scale data pipelines.
• Experience with Docker, Kubernetes, and cloud-native technologies.
• Experience driving operational excellence through observability, automation, and reliability engineering.
• Hands-on experience applying AI/ML techniques to engineering challenges, including anomaly detection, predictive analytics, or intelligent automation.
• Contributions to large-scale infrastructure or open-source projects.
• Passion for building inclusive, collaborative teams and developing future technical leaders.