Technical Solutions Architect II -- Data Engineer

Work Experience: A minimum of 3 years of relevant technical experience across data engineering and data science, including hands on experience preparing and managing data for data science use cases. Customer facing sales experience is required, gained in a presales, consulting, solutions engineering, reseller, or systems integrator setting. The ideal candidate is a data scientist who has had to prepare and manage their own data, or a data engineer who has worked closely with data scientists and understands their needs.

Specialized Knowledge, Skills, and Abilities:

• Hands on experience with Snowflake and/or Databricks is highly desirable, as most customers we meet use one or both. Exposure to Microsoft Fabric and similar modern data platforms is also relevant.

• Data engineering fundamentals, including data ingestion and integration, ETL and ELT pipeline design, orchestration, batch and streaming processing, SQL, Python, and Apache Spark, data modeling, data quality and observability, and metadata and lineage.

• Working data science experience, including feature engineering, model development and evaluation, and experimentation. A PhD is not required. The requirement is enough depth to hold credible peer conversations with customer data scientists and to understand what they need from data platforms.

• Practical understanding of how AI workloads, including large language models (LLMs), retrieval augmented generation (RAG), and agentic AI, consume enterprise data. The emphasis is on explaining why data readiness determines whether these solutions work, not on building agents for customers.

• Cloud familiarity from a data and AI perspective, including the ability to move and use data from AWS, Azure, or Google Cloud environments in AI initiatives. Deep cloud infrastructure expertise is not required.

• Working knowledge of enterprise data governance, security, and data strategy, and how trusted data enables successful AI initiatives.

• Strong communication skills, with the ability to speak with data engineers, data scientists, architects, developers, IT leadership, and executive stakeholders, tailoring technical depth appropriately while maintaining credibility with every audience.

• Advisory mindset and the ability to lead customers through ambiguous technical challenges, structure discovery conversations, identify gaps, evaluate platform tradeoffs objectively, and deliver actionable recommendations.

• Sales experience and commercial awareness to support account teams effectively in fast moving sales cycles, including working as a guest in accounts owned by others.

• Genuine curiosity and a habit of staying current. The technologies at the center of this role are new, and the right candidate has learned them by using them and is actively exploring how AI is changing the way data work gets done.