Senior Software Engineer; Analytics Compute Platform team

About the role

This role focuses on software engineering where you will turn product ideas into production code within small, cross-functional teams. You will work closely with other engineers to review code and ship features in small, manageable batches following agile practices like sprints and daily standups. A significant part of your week will involve planning sessions, code reviews, and deep debugging sessions, not just writing new code. The role values the ability to explain complex technical decisions in plain language as much as it values strong technical execution. You will own the implementation of features that power the core analytics compute infrastructure used by multiple product teams. Your work will directly influence the performance, reliability, and usability of the platform that enables data-driven decisions. You will be responsible for resolving ambiguous, cross-team problems from start to finish, ensuring successful delivery of complex initiatives.

Key facts

What you'll do

Demonstrate strong backend or distributed-systems engineering experience, with exposure to OLAP databases, query engines, or data infrastructure.

Utilize SQL, query planning, and execution to build robust APIs that become critical dependencies for numerous other engineering teams.

Comfortably analyze and reason about tradeoffs between flexibility, performance, and ease of use to make deliberate architectural decisions.

Own ambiguous, cross-team projects from start to finish, rather than only executing on well-scoped, isolated tasks.

Collaborate effectively across functions, understanding that the team's work derives value only when products are successfully built on top of the compute layer.

Design and implement semantic layers, ORMs, and other data-modeling abstractions to simplify how engineers interact with complex data.

Work with columnar databases and OLAP query engines to ensure that analytics workloads are performant and scalable at large scale.

Integrate the data platform with external tools and AI/agent workflows to expand the platform's capabilities and reach.

Engage with modern data platforms, event-driven architectures, and API design patterns specific to analytics workloads.

Ensure that SQL-based query engines are optimized and that semantic layer abstractions provide clarity and efficiency to consumers of the platform.

Contribute to the design of data-modeling patterns that make analytical data more accessible and actionable for non-technical stakeholders.

Participate in the full lifecycle of feature development, including requirements gathering, design, implementation, testing, and post-launch monitoring.

Help mentor junior engineers by providing code reviews, technical guidance, and examples of high-quality software craftsmanship.

Champion best practices in testing, debugging, and performance optimization to maintain a high standard of code quality across the team.

Requirements

Bring strong backend or distributed-systems engineering experience, with exposure to OLAP databases, query engines, or data infrastructure.

Demonstrate experience with SQL, query planning or execution, and building APIs that many other engineering teams depend on.

Comfortably reason about tradeoffs between flexibility, performance, and ease of use, and make deliberate decisions among them.

Have a track record of owning ambiguous, cross-team projects end-to-end rather than only executing well-scoped tasks.

Collaborate effectively across functions, recognizing that the team's work only matters in the context of products built on top of the compute layer.

Nice to have

Show experience with large-scale event or time-series data systems.

Bring background designing semantic layers, ORMs, or other data-modeling abstractions.

Know columnar databases or OLAP query engines.

Have experience integrating a data platform with external tools or AI/agent workflows.

Skills & tools

Work with SQL-based query engines, semantic layer abstractions, and data-modeling patterns.

Engage with modern data platforms, event-driven architectures, and API design for analytics workloads.

Practical notes

This role operates primarily from San Francisco office locations, with potential for occasional remote work as defined by team agreements.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Analytics compute platforms translate user questions into optimized queries against data warehouses.

SQL and query-planning expertise are central to performance and reliability in analytical systems.

Cross-functional collaboration is essential because compute teams enable product and data teams to build features.

OLAP concepts such as columnar storage and vectorized execution influence design decisions in analytics workloads.

Modern data platforms often integrate semantic layers to decouple business logic from raw storage.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.