Senior Software Engineer

Yahoo serves as a trusted guide for hundreds of millions of people globally, helping them achieve their goals online through our portfolio of iconic products. For advertisers, Yahoo Advertising offers omnichannel solutions and powerful data to engage with our brands and deliver results.

The ideal candidate will have strong distributed systems knowledge and AI/ML experience to design, build, and optimize scalable data pipelines, and infrastructure that power advanced analytics and machine learning solutions. In this role, you will collaborate closely with software engineers, product owners and business stakeholders to prepare and transform large datasets(realtime pipelines), support end-to-end development and deployment, and ensure robust, efficient, and secure data flows. You will leverage your expertise in cloud platforms, big data tools, and machine learning frameworks to drive innovation and deliver actionable insights that advance our organization’s AI initiatives and business objectives.

Little About Us: 

Yahoo’s Central Data team manages massive scale (100+ petabyte) data systems to glean insights on Yahoo! products and to improve the experience for its 1B+ user base. The team provides the foundations for the user engagement data collection and processing for all of Yahoo’s users, Operational Excellence, Anomaly detection and Governance across the organization. Your work will directly influence product changes and you will work on a team of talented and motivated engineers to improve the user experience on popular Yahoo! sites and apps like Yahoo Mail & Homepage, Yahoo Sports, Yahoo Finance, Yahoo News and many other new products.

A Lot about You:

• Apply software engineering expertise to build high-performance, scalable data warehouses.

• Be excited to learn and take ownership for large-scale projects spanning many tech stacks and environments.

• Design, build, and launch efficient & reliable data pipelines to move and transform data on the scale of multiple petabyte(s) using the latest technologies.

• Build real time analytics and ingestion pipelines capable of processing more than a million events per second and provide insights at sub-second latency

• Interact with product owners and end users to understand and solve new business requirements as they emerge.

• Design and audit processes for ensuring the delivery of high-quality data through rigorous QA checks

• Have excellent data modeling skills to understand the nuances of various dimension and metric types in warehouses.

• Design workflows to ingest, load and present new data sets for users.

• Provide active support, be on rotation for on-call support on production pipelines (typically a couple of times each quarter).

• Define and manage SLA for all data sets in allocated areas of ownership.

• Work with the production engineering / infrastructure team to drive resolution to production issues.

Required Skills:

• BS/MS in Computer Science and/or Mathematics/Statistics

• 4+ years experience in relevant software development with at least 2 years of professional Java and/or Python experience

• 2+ years experience in the Big Data pipeline and analytics space with experience across technology stacks.

• 2+ years experience in custom ETL design using Big Data stack environments (Hadoop, MapReduce, Pig, Hive, AWS EMR, Apache Beam, Google Cloud Platform Dataflow, BigQuery), implementation and maintenance.

Preferred Experience:

• Experience or familiarity with some of the following tools: Kafka, Storm, Streaming (Spark,Dataflow), ElasticSearch

• Design, build, and maintain scalable data pipelines and ETL processes to support machine learning and AI initiatives on Google Cloud Platform (GCP).

• Implement and optimize data storage solutions using GCP services such as BigQuery, Cloud Storage, and Dataflow.

• Ensure data quality, integrity, and security throughout the data lifecycle.

• Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver actionable insights.

• Monitor, troubleshoot, and maintain the health and performance of cloud-based data infrastructure.

• Automate manual processes and repetitive tasks to improve efficiency and reduce errors.

• Apply data