Senior Data Engineer

Senior Data Engineer
Dallas, TX or Mountain View, CA (Only Locals)
Visa: OPT, H4EAD, USC (GC only on w2)


As part of the Mail Analytics 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're passionate about building data infrastructure and platforms that power modern Data- and AI-driven business at scale, we want to hear from you!

Your Day
● 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 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
Must Have
● BS in Computer Science/Engineering, relevant technical field, or equivalent practical experience, with specialization in Data Engineering 
● 8+ years of experience building scalable ETL pipelines on industry standard ETL orchestration tools (Airflow, Composer, Oozie) with deep expertise in SQL, PySpark, or scala. 
● 5+ years leading data engineering development directly with business or data science partners 
● 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 frameworks 
● Worked within standard GitOps workflow (branch and merge, PRs, CI / CD systems)