Uber - Staff Data Engineer - Scala/Python
4 weeks ago
The Comms Data Engineering team enables high-quality, data-driven decision-making that improves the way Uber supports Communications, the cost incurred and optimizations.
We are responsible for building and maintaining the foundational data infrastructure around Commumications platform.
Our systems serve thousands of support professionals and power real-time dashboards, operational alerts, ML-driven recommendations, and long-term strategy planning.
As a Staff Data Engineer, you will be a technical anchor for the team setting the vision for scalable data architecture and ensuring technical excellence across every layer of the stack.
You'll have a unique opportunity to influence not just engineering outcomes, but how the company understands and evolves the Communication experience.
We are looking for engineers who are passionate about solving complex data problems at scale, enjoy working in high-ownership environments, and want to make a real difference in how we support users at Uber.
About The Role :
We are looking for a Staff Data Engineer to lead strategic initiatives within the Comms Data Engineering team.
In this high-impact role, you will architect and scale data systems that enable visibility, automation, and performance optimization across Communication cost optimizations.
You will drive end-to-end ownership of complex data solutions, collaborating with cross-functional partners in Engineering, Product, and Support Operations to transform raw data into actionable insights and reliable platforms.
The ideal candidate is a hands-on technical leader who thrives in ambiguity, influences cross-org decision-making, and has a deep passion for improving customer experiences through data.
What The Candidate Will Need / Bonus Points the Candidate Will Do :
- Experience building real-time or near real-time data platforms using Kafka, Flink, or similar technologies.
- Familiarity with Airflow, Oozie, or other workflow orchestration tools.
- Background in communication platform / paid marketting at scale is a plus.
- Track record of mentoring engineers and establishing best practices in data architecture, testing, and observability.
- Ability to balance long-term technical vision with short-term delivery priorities.
- Passion for data governance, data quality, and building platforms that scale with the business.
Basic Qualifications :
- 8+ years of software/data engineering experience, including architecting large-scale data pipelines and systems.
- Strong programming skills in Python, Java, or Scala.
- Expertise in distributed data processing technologies such as Apache Spark, Flink, Hive, or similar.
- Deep understanding of data warehousing, dimensional modeling, and ETL/ELT design patterns.
- Proven experience working with stakeholders across Engineering, Product, and Business to define and execute on complex data requirements.
- Strong communication and leadership skills, with the ability to influence without Qualifications :
- Experience building real-time or near real-time data platforms using Kafka, Flink, or similar technologies.
- Familiarity with Airflow, Oozie, or other workflow orchestration tools.
- Background in customer support, operations analytics, or case management systems is a plus.
- Track record of mentoring engineers and establishing best practices in data architecture, testing, and observability.
- Ability to balance long-term technical vision with short-term delivery priorities.
- Passion for data governance, data quality, and building platforms that scale with the business.
(ref:hirist.tech)
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