Specialist Solutions Architect - Data Engineering
Databricks · Amsterdam, Netherlands · Field Engineering - Other · listed October 14, 2026
The shape of it
Seniority
Principal
Where
Hybrid
Requirements listed
5
Length
748 words
In the posting’s own words
As a Specialist Solutions Architect (SSA) – Data Engineering, you are a hands-on technical authority who guides strategic customers in designing, building, and optimizing production-grade data workloads on the Databricks Data Intelligence Platform. Partnering closely with Account Teams and Solution Architects, you translate complex big data challenges into high-performance, scalable architectures using Apache Spark™ and modern streaming frameworks. In this role, you will drive successful implementations, align technical roadmaps to accelerate platform adoption, and establish deep domain specialization; whether in real-time streaming, performance optimization, or operational data layers, while expanding your impact across Field Engineering.
What it asks for · 5
- Deep Data Engineering Expertise: 5+ years in data or software engineering with hands-on proficiency in distributed processing (Apache Spark™, Delta Lake, SDP) and real-time streaming technologies (Spark Streaming, Kafka, CDC).
- AI-First Builder Mindset: Demonstrated daily reliance on AI coding tools and LLMs to accelerate pipeline development, troubleshooting, and asset generation.
- Production Programming & Foundations: Strong production coding skills in Python, SQL, and Scala or Java, with a solid grasp of data modeling and system design fundamentals.
- Customer Advisory & Delivery: 5+ years in pre-sales or post-sales technical consulting, managing customer engagements and navigating multi-stakeholder technical discussions.
- Operational & HTAP Architectures: Practical experience with operational/transactional data layers (Postgres/OLTP), low-latency serving, reverse ETL, and hybrid transactional/analytical processing.
Also a plus
- Cloud & Migration Mastery: Hands-on experience with major cloud platforms (AWS, Azure, GCP) and legacy data lake modernisation (e.g., Hadoop migration and cost optimisation).
- Application Experience: Background in constructing specialised data applications (e.g., risk modelling, fraud detection, customer lifetime value) across industries as a data expert.
- Ecosystem & Culture Contribution: Active track record of technical community engagement, public speaking, open-source contributions, or peer mentoring and interviewing.
- Flexibility & Travel: Ability to travel up to 30% for strategic customer engagements and internal alignment.
What the job covers
- Drive Technical Wins & Scope: Lead end-to-end technical design, proof of concepts (PoCs), and workload sizing for strategic customer engagements, acting as a trusted advisor to front-line technical stakeholders to secure technical wins.
- Architect Production Data Systems: Design, build, and optimize scalable, high-performance batch and streaming pipelines, operational data layers (Lakebase), and end-to-end performance load testing on Databricks.
- Deploy Interactive Data Applications: Build and operationalize custom user-facing data applications using Databricks Apps, transforming data pipelines and AI models into interactive tools.
- AI-Native Engineering: Leverage AI coding assistants, LLMs, and agentic workflows (e.g., Genie Code, Cursor) as a force multiplier to rapidly prototype, debug, and deliver architectural assets with full ownership of outputs.
- Enablement & Domain Leadership: Mentor peers, contribute reusable architecture assets, and deliver technical workshops and community presentations to scale expertise across accounts and field teams.
Tools and skills named
Data
- Spark5×
- Databricks3×
- Data modeling
- Data pipelines
- Data warehouse
- ETL
- Hadoop
- PostgreSQL
Cloud & infra
- AWS
- Azure
- GCP
- Kafka
Languages
- Java
- Python
- Scala
- SQL
Go to market
- Pipeline generation
- Solutions architecture
Models & research
- LLM2×
Ways of working
- Mentorship
- Testing
Words the posting leans on
- data17×
- technical10×
- customer7×
- engineering5×
- streaming5×
- architect3×
- architectures3×
- assets3×
- coding3×
- customer engagements3×
- data applications3×
- data layers3×
- design3×
- e.g3×
- experience3×
- hands-on3×
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