Req: FEQ127R163
Location: London
Recruiter: Dina Hussain
Skills: Data Engineering/DWH
As a Specialist Solutions Architect (SSA) - Data Engineering, you will guide customers in building big data solutions on Databricks that span a large variety of use cases. You will be in a customer-facing role, working with and supporting Solution Architects, and will require hands-on production experience with Apache Spark™ and expertise in other data technologies. SSEs help customers through the design and successful implementation of essential workloads while aligning their technical roadmap to expand the use of the Databricks Data Intelligence Platform. As a deep go-to-expert reporting to the Senior 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 streaming, performance tuning, 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 data pipelines, including end-to-end pipeline load performance testing and optimisation
- Become a technical expert in an area such as data lake technology, big data streaming, or big data ingestion and workflows
- Assist Solution Architects with more advanced aspects of the technical sale, including custom proof of concept 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:
- Extensive experience in a customer-facing technical role. Pre-sales or post-sales experience working with external clients across a variety of industry markets
- Nice to have: Databricks Certification
- Travelling approx. 20-30% of the time
Data Engineer Skills
- Experience as a Data Engineer: query tuning, performance tuning, troubleshooting, and debugging Spark or other big data solutions.
- Extensive experience building big data pipelines
- Experience in maintaining and extending production data systems to evolve with complex needs
- Deep Speciality Expertise in at least one of the following areas:
- Experience scaling big data workloads (such as ETL) that are performant and cost-effective
- Experience migrating Hadoop workloads to the public cloud - AWS, Azure, or GCP
- Experience with large-scale data ingestion pipelines and data migrations - including CDC and streaming ingestion pipelines
- Expert with cloud data lake technologies - such as Delta and Delta Live
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience
- Production programming experience in SQL and Python, Scala, or Java
- Professional experience with Big Data technologies (Ex: Spark, Hadoop, Kafka) and architectures
Data Warehousing, Database Skills
- Experience with the design and implementation of a broad range of analytical and transactional 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)
- Hands-on experience with RDBMS systems (PostGres, MySQL, SQL Server, Oracle, MariaDB)
- Experience in SQL language or any SQL dialect (PL/SQL, Transact-SQL or others)
- Experience with BI tools such as 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 PySpark.
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.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
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.
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Equity Value & Accessibility — Equity grants and RSUs are a major part of total compensation and are highlighted for meaningful upside potential. Stock-based awards and refreshers contribute to strong overall pay positioning across senior technical and go-to-market roles.
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Healthcare Strength — Medical, dental, and vision coverage are complemented by mental-health resources, an EAP, and wellness reimbursements. Health benefits are consistently framed as comprehensive and competitive.
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Parental & Family Support — Paid parental leave for all parents, fertility support, and backup care options provide tangible assistance for family needs. Hybrid work norms and team-day structure further ease coordination for caregivers.
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What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
