Senior Data Engineer

Sterling B2B Developer

Role: Senior Data Engineer

Location: Dallas, TX or Mountain View, CA

Duration: Long Term Contract

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