Senior Data & AI Engineer

About the role

At Viewnear, we help enterprises stand up two capabilities they keep: a data practice their teams trust and an AI practice that ships use cases into production, built on Snowflake, run by the client's own team, and guided and accelerated by ours.

We're looking for a Senior Data & AI Engineer who brings strong technical depth, ownership, and execution discipline to the data and AI practices we build with clients.

This role is for someone who knows that great data and AI work is not built through big talk. It is built through clean architecture, reliable pipelines, thoughtful modeling, strong engineering habits, and the willingness to solve complex problems when the path is not perfectly clear.

The role helps design, build, and scale modern data and AI solutions on Snowflake, turning complex business operations into trusted, usable, production-ready systems.

What the role involves

This engineer leads the design and implementation of governed data foundations, pipelines, data models, and AI-ready architectures using Snowflake as the core cloud data platform.

The work runs close to business, analytics, engineering, and leadership teams: understanding operational challenges, translating them into technical requirements, and delivering solutions that create measurable business value.

It also means bringing AI use cases from concept to production: preparing trusted data, building scalable integration patterns, and supporting solutions such as LLM applications, RAG architectures, semantic search, automation workflows, and AI-powered analytics.

This is a hands-on role: writing SQL and Python, designing data models, building ELT pipelines, reviewing technical designs, troubleshooting performance issues, documenting decisions, and mentoring others on the team.

What we're looking for

Strong experience across:

• Snowflake architecture, development, performance tuning, cost optimization, and security
• Data engineering with SQL, Python, Snowpark, Snowflake Cortex, Tasks, Streams, Dynamic Tables, and Stored Procedures
• ELT pipeline design, data modeling, dimensional modeling, semantic layers, and analytics engineering
• Data integration patterns using APIs, files, external stages, cloud storage, and orchestration tools
• AI and ML enablement: trusted data pipelines, vector search, RAG patterns, LLM integrations, and production AI workflows
• Building production-grade systems with testing, monitoring, governance, access control, documentation, and cost awareness
• Working directly with business stakeholders to clarify needs and turn them into practical technical solutions
• Leading technical conversations without ego and raising the quality bar for the team

Experience with dbt, Airflow, Coalesce, Azure, AWS, or GCP is a plus.

The kind of person who succeeds here

The people who thrive here are the ones who keep showing up.

They take ownership of messy source systems, unclear requirements, broken pipelines, performance bottlenecks, and ambitious goals, then work through them with patience and precision.

They do not need everything to be perfect before starting. They know how to ask the right questions, make smart tradeoffs, and move the work forward.

They care about clean architecture, but they care just as much about getting useful solutions into people's hands. They understand that trust in data is earned one correct number, one reliable pipeline, and one well-built solution at a time.

They bring technical depth, and just as much humility. They help others get better. They make the team stronger.

What success looks like

Success means our clients trust their data, AI use cases move beyond demos, pipelines run reliably, and business teams make faster, better decisions.

This role helps build the governed foundation on Snowflake that turns ambitious ideas into real systems. We're looking for someone ready to do the work, carry responsibility, and help the team win.