Req ID FEQ225R49

As a Senior Specialist Solutions Engineer (SSE), you will guide customers in building big data solutions on Databricks that span a large variety of use cases. These are customer-facing roles, working with and supporting the Solution Architects, requiring hands-on production experience with Apache Spark™ and expertise in other data technologies. SSAs help customers through the design and successful implementation of essential workloads while aligning their technical roadmap for expanding the usage of the Databricks Data Intelligence Platform. As a deep go-to-expert reporting to the Specialist Field Engineering Manager, 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.  

You will be reporting to Manager, Field Engineering (Specialist Team)

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 optimisation
• Provide technical expertise 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 proof of concept content, estimating workload sizing, and custom architectures
• Improve community adoption (through tutorials, training, hackathons, conference presentations)
• Contribute to the Databricks Community

What we look for:

• Experienced, technical, and customer-facing, with a background in Data Engineering and Data Warehousing skills, I am looking to learn and develop in a customer-facing technical role as a subject matter expert (SME) in a pre-sales environment.
• Pre-sales or post-sales experience working with external clients across a variety of industry markets

Data Engineer Skills

• Experience as a Data Engineer: query tuning, performance tuning, troubleshooting, and debugging Spark or other big data solutions.
• Experience with big data technologies such as Spark/Delta, Hadoop, NoSQL, MPP, and OLAP.
• Experience with cloud architecture, systems, and principles.
• Production programming experience in Python, R, Scala or Java.
• Deep expertise in at least one of the following areas:
  • Scaling ETL pipelines that are performant and cost-effective.
  • Tuning queries on big data.
  • Development tools and best practices for data engineers including CI/CD, unit and integration testing, and automation and orchestration
  • Building and scaling streaming pipelines
• Knowledgeable in a core Big Data Analytics domain with some exposure to advanced proofs-of-concept and an understanding of a major public cloud platform (AWS, GCP, Azure)
• Nice to have: Databricks Certification

• Travelling approx. 20-30% of the time

Data Warehousing, Business Intelligence Skills

• Experience with the design and implementation of a broad range of data technologies such as Hadoop, Apache Spark™, NoSQL, OLTP, OLAP, and ETL/ELT.
• Hands-on experience working with MPP data warehouse appliances (Oracle Exadata, Teradata, IBM Netezza) or cloud data warehouses (Amazon Redshift, Azure Synapse, Snowflake)
• Experience in SQL language or any SQL dialect (PL/SQL, Transact-SQL or others)
• Experience with BI tools such Power BI, Tableau, Qlik, or others
• Knowledge of development tools and best practices for data engineers including CI/CD, unit and integration testing, plus automation and orchestration
• Expertise in data warehousing - such as query tuning, performance tuning, troubleshooting, and debugging MPP data warehouses or other big data solutions. Maintained, extended, or migrated a production data warehouse system to evolve with complex customer needs.
• Production programming experience in one of the following languages - Python, Scala, or R

Benefits

• Private medical insurance
• Private dental insurance
• Health Cash Plan
• Life, income protection & critical illness insurance
• Pension PlanEquity awards
• Enhanced Parental Leaves
• Fitness reimbursement
• Annual career development fund
• Home office & work headphones reimbursement
• Business travel accident insurance
• Mental wellness resources
• Employee referral bonus

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.