Databricks Architect - Lakehouse Modernization

About the role

We are looking for an architect who builds, not one who documents.

At Anblicks, our architects sit at the center of complex data and AI programs - connecting source systems, applications, cloud infrastructure, data platforms, and the downstream consumers who actually use the output. You will own the end-to-end picture: how data enters the organization, how it moves, where it lands, how it is governed, and how business teams get value from it.

This is a hands-on role. You will be in the room when the hard problems come up, and you will be expected to solve them - with a whiteboard, with a blueprint, and with working code when that is what it takes.

What youll do

• Own end-to-end solution architecture across data platforms, applications, integration layers, cloud infrastructure, and downstream consumption.
• Assess current-state landscapes and design target-state architectures that are realistic, sequenced, and implementable - not aspirational diagrams.
• Build reference implementations, blueprint code, and proof-of-concepts that give delivery teams a working starting point.
• Identify automation opportunities relentlessly - in pipelines, in deployment, in testing, in operations, in the architecture practice itself.
• Make and defend technology choices: build vs. buy, batch vs. streaming, managed vs. self-hosted, and the trade-offs behind each.
• Build observability into designs - monitoring, alerting, and the signals teams need to run the solution in production.
• Act as escalation support across delivery teams, driving root-cause analysis on hard production and data issues.
• Partner with engineering teams through delivery. You stay involved past the design review.
• Represent Anblicks directly with clients - lead architecture discussions, discovery workshops, design reviews, and solution walkthroughs on client calls.
• Translate between business and technology - explain architecture to a CFO and a platform engineer on the same day, at the right altitude for each.
• Stay current. Evaluate emerging tooling and bring whats genuinely useful into our practice.

What were looking for

Breadth across the enterprise stack You understand how an organization actually fits together - applications, source systems, integration patterns, cloud infrastructure, data platforms, and downstream analytics and AI consumers. You do not need to be the deepest expert in any single one, but you need to know how they connect and where they break.

Cloud essentials

• Event-driven and streaming architectures - messaging, queues, pub/sub, event sourcing, and when not to use them.
• Cloud infrastructure at a working level - compute instances, VPCs and subnets, security groups, load balancing, IAM, and basic networking. You should be comfortable reasoning about how an EC2 instance is configured or tracing a network path end to end.
• API and integration patterns across systems.

Data platform depth Hands-on production experience with Databricks or Snowflake - modeling, performance, cost, ingestion patterns, and governance. Experience with both is a plus.

Observability and debugging You design monitoring and alerting into a solution rather than bolting it on afterward. You can debug across the stack - application, integration, infrastructure, and network - and you go deep when the problem is in the data: pipeline failures, silent data quality issues, skew, performance regressions, cost spikes. When another team is stuck, youre the one they call, and you help them find the root cause instead of taking the ticket off their hands.

Hands-on problem solving You can open an IDE and produce working code to prove a pattern, unblock a team, or validate a design. Using AI coding tools to get there faster is not just accepted - its encouraged. What matters is that you can solve the problem and stand behind the solution.

Automation instinct Your first reaction to repeated manual work is to ask why it isnt automated yet.

Articulation You can explain a design to a business stakeholder in outcomes and trade-offs, and to an engineering team in specifics - and youre equally credible in both rooms.

Client-facing experience (required) This is a client-facing role. You will be on client calls regularly - presenting architecture, running workshops, handling pushback, and building confidence with senior stakeholders. You should have a track record of owning the technical relationship with a client, not working behind an account or delivery lead.

Experience 15-20+ years in technology, with meaningful time in architecture roles on complex, multi-system environments.

Nice to have

• GenAI and ML platform architecture - RAG patterns, model serving, vector stores, evaluation and cost management.
• Data governance, lineage, and security frameworks at enterprise scale.
• Relevant cloud architecture certifications (useful signal, not a substitute for hands-on ability).

Why Anblicks

Youll work across industries and architectures rather than inside one stack for years. Our problems are real, varied, and consequential - and we back architects who want to stay technical.