Senior Data Engineer

Senior Data Engineer (Finance)

Yahoo Mail is the ultimate consumer inbox with hundreds of millions of users. It's the best way to access your email and stay organized from a computer, phone or tablet. With its beautiful design and lightning fast speed, Yahoo Mail makes reading, organizing, and sending emails easier than ever.

The Mail Analytics Infrastructure & Data Engineering team at Yahoo plays a fundamental role in enabling Yahoo Mail's success by building mission critical data serving, pipelines, lakehouse, core data sets, frameworks, tooling, analytics systems and infrastructures to support the ever-growing demand for Data and analytics. We are constantly pushing the envelope of data & analytics platforms due to the massive volume of data we need to harness in order to drive the explosive growth of AI advances.

As a Senior Data Engineer, you will work to define the data ontology for all of Yahoo Mail, establish standard methodologies for data operations and lifecycle management, design and build analytics tooling and frameworks, and influence event instrumentation. Additionally, this role is highly multi‑functional, requiring close collaboration with Data Science and Machine Learning teams to understand customer requirements and analytics applications, as well as with other Mail engineering teams to develop integrated solutions.

As part of the Mail Analytics Infrastructure & Data Engineering team, you will be working on large‑scale batch pipelines, data serving, data lakehouse, and analytics systems, enabling mission critical decision making, downstream, AI‑powered capabilities, and more.

If you thrive on building data infrastructure and platforms that power modern data‑ and AI‑driven businesses at scale, we'd love to hear from you!

• Partner with Data Science, Product, and Engineering to collect requirements to define the data ontology for Mail Data & Analytics
• Lead and mentor junior Data Engineers to support Yahoo Mail's ever‑evolving data needs
• Design, build, and maintain efficient and reliable batch data pipelines to populate core data sets
• Develop scalable frameworks and tooling to automate analytics workflows and streamline users interactions with data products
• Establish and promote standard methodologies for data operations and lifecycle management
• Develop new or improve and maintain existing large‑scale data infrastructures and systems for data processing or serving, optimizing complex code through advanced algorithmic concepts and in‑depth understanding of underlying data system stacks
• Create and contribute to frameworks that improve the efficacy of the management and deployment of data platforms and systems, while working with data infrastructure to triage and resolve issues
• Prototype new metrics or data systems
• Define and manage Service Level Agreements for all data sets in allocated areas of ownership
• Develop complex queries, very large volume data pipelines, and analytics applications to solve analytics and data engineering problems
• Collaborate with engineers, data scientists, and product managers to understand business problems, technical requirements to deliver data solutions
• Engineering consulting on large and complex data lakehouse data

• BS in Computer Science/Engineering, relevant technical field, or equivalent practical experience, with specialization in Data Engineering
• 6+ years of experience building scalable ETL pipelines on industry standard ETL orchestration tools (Airflow, Composer, Oozie) with deep expertise in SQL, PySpark, or scala.
• Built, scaled, and maintained Multi‑Terabyte data sets and having an expansive toolbox for debugging and unblocking large scale analytics challenges (skew mitigation, sampling strategies, accumulation patterns, data sketches, etc.)
• Experience with at least one major cloud's suite of offerings (AWS, GCP, Azure).
• Developed or enhanced ETL orchestrations tools or framework
• Worked within standard Git Ops workflow (branch and merge, PRs, CI / CD systems)
• Experience working with GDPR
• Highly self‑motivated with a strong sense of ownership
• Detail‑oriented with a commitment to quality and accuracy
• Collaborative…