Forward Deployed Engineer

Location: Mountain View, CA — On-site

ABOUT GRANICA

Granica is building the efficiency and intelligence layer for enterprise AI.

Enterprises already operate enormous proprietary data estates and increasingly build AI systems on top of them, but the underlying infrastructure remains fragmented, expensive, and inefficient.

Granica is building across three related layers:

- Crunch — efficiency for enterprise data

- Large Tabular Models — intelligence for enterprise structured data

- Myelin — efficiency for stateful, long-running AI agents

Crunch continuously optimizes large enterprise lakehouse environments across technologies such as Apache Spark, Iceberg, Delta Lake, Parquet, Snowflake, and Databricks — reducing storage and compute cost while improving data layout and operational efficiency.

Granica has already processed hundreds of petabytes of customer data for companies including Snap, Pinterest, Lyft, and ShareChat, with customers able to validate meaningful savings within weeks.

ABOUT THE ROLE

Granica is hiring a Forward Deployed Engineer to become the technical bridge between our customers, Engineering, Product, and GTM teams.

You will work directly inside complex enterprise data environments to understand workloads, investigate performance and cost problems, deploy Granica, run technical evaluations, and turn the results into clear customer recommendations.

This is a highly hands-on role. You should be comfortable moving from a customer conversation to Spark debugging, SQL analysis, performance benchmarking, architecture decisions, and an executive-facing explanation of the technical and economic tradeoffs.

As one of Granica’s first dedicated Forward Deployed Engineers, you will also help define how the function operates as we scale.

WHAT YOU’LL DO

- Lead technical discovery with enterprise data, infrastructure, and AI teams

- Understand customer data architecture, workloads, constraints, and business priorities

- Deploy and configure Granica in complex customer-controlled environments

- Investigate performance, reliability, storage, and compute-cost problems across large data platforms

- Debug and optimize Apache Spark workloads and related distributed-data systems

- Analyze table layouts, file structure, partitioning, compaction, and data-maintenance behavior

- Design and execute technical evaluations and proofs of value with clear success criteria

- Establish trustworthy measurements and translate results into defensible customer recommendations

- Explain technical tradeoffs across performance, cost, reliability, and operational complexity

- Partner with Account Executives during technical discovery, evaluations, and enterprise sales cycles

- Present findings and business value to engineers, architects, and executive stakeholders

- Translate recurring customer problems into Product and Engineering priorities

- Build repeatable deployment, troubleshooting, benchmarking, and evaluation playbooks

WHAT WE’RE LOOKING FOR

- 5+ years in Forward Deployed Engineering, Data Engineering, Solutions Architecture, Sales Engineering, or a similarly hands-on technical role

- Strong hands-on Apache Spark experience, including production workloads, debugging, performance tuning, and distributed execution behavior

- Strong understanding of distributed data systems and modern cloud data infrastructure

- Experience with lakehouse technologies such as Apache Iceberg, Delta Lake, Parquet, Hive, or comparable systems

- Strong SQL skills and proficiency in Python, Scala, Java, or another relevant programming language

- Experience diagnosing performance or reliability problems using data and experimentation

- Ability to reason about infrastructure economics, including storage, compute, and performance tradeoffs

- Strong customer communication skills and the ability to explain technical recommendations clearly

- High ownership and comfort operating without an established playbook

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