Sr. Specialist Solutions Engineer

Being part of the Specialist Solutions Architect (SSA) organisation, you will guide customers in building big data solutions on Databricks that span a large variety of use cases. You will be in a leading customer-facing role, working alongside and in support of the Solution Architects, which requires hands-on production experience with Apache Spark and expertise in other data technologies. You will help customers through design and successful implementation of critical workloads while aligning tier technical roadmap for expanding the usage of the Databricks Lakehouse Platform. As a deep go-to-expert reporting to the Technical GM, you will continue to strengthen your technical skills through mentorship, learning and internal training programs and establish yourself in an area of speciality - whether that be performance tuning, machine learning, industry expertise, or more.

The impact you will have:

• Provide technical leadership to guide strategic customers to successful implementations on big data projects, ranging from architectural design to data engineering to model deployment.
• Architect production level workloads, including end-to-end pipeline load performance testing and optimization.
• Become a technical expert in an area such as data management, cloud platforms, data science, machine learning, or architecture.
• Assist Solution Architects with more advanced aspects of the technical sale including custom POC content, estimating workload sizing, and custom architectures.
• Provide tutorials and training to improve community adoption (including hackathons and conference presentations)
• Contribute to the Databricks Community

What we look for:

• 3+ years experience in a customer-facing technical role with expertise in at least one of the following:
  • Software Engineer/Data Engineer: query tuning, performance tuning, troubleshooting, and debugging Spark or other big data solutions.
  • Data Scientist/ML Engineer: model selection, model lifecycle, hyperparameter tuning, model serving, deep learning
  • Data Applications Engineer: build out use cases that extensively utilise data - risk modelling, fraud detection, customer life-time value, etc.
• Experience with design and implementation of big data technologies such as Spark/Delta, Hadoop, NoSQL, MPP, OLTP, and OLAP.
• Maintain and extend production data systems to evolve with complex business needs.
• Production programming experience in Python, R, Scala or Java.
• Deep Specialty Expertise in at least one of the following areas:
• Experience with scaling big data workloads that are performant and cost effective
• Experience with Development Tools for CI/CD, Unit and Integration testing, Automation and Orchestration, REST API, BI tools and SQL Interfaces, e.g. Jenkins
• Experience designing data solutions on cloud infrastructure and services, such as AWS, Azure or GCP utilising best practices on cloud security and networking.
• Experience with ML concepts covering Model Tracking, Model Serving and other aspects of productionizing ML pipelines in distributed data processing environments like Apache Spark, using tools like MLflow.
• Experience in implementing industry specific data analytics use cases.
• Degree in quantitative discipline (Computer Science, Applied Mathematics, Operations Research)

Benefits

• Benefits allowance
• Equity awards
• Paid parental leave
• Gym reimbursement
• Annual personal development fund
• Work headphones reimbursement
• Business travel insurance