Staff Engineer - Analytics & Data Architecture

Oscilar

2d•$220,000 - $262,500•Remote

About The Position

At Oscilar, we're building the most advanced AI Risk Decisioning™ Platform. Banks, fintechs, and digitally native organizations rely on us to manage their fraud, credit, and compliance risk with the power of AI. This role will own the data layer behind our analytics product and design of our analytics architecture and data model. The current setup is a high-performance analytical store serving customer-facing queries. The future vision is a tiered platform that separates the system of record, a low-latency serving tier, and a batch/ML tier. The engineer will ensure the serving layer remains fast and reliable while designing and building the broader architecture, making decisions on workload placement based on measured latency, cost, and isolation. This is a hands-on engineering role with significant architectural influence, involving both coding and shaping the multi-quarter direction of the analytics platform.

Requirements

Deep, production experience operating a database or analytical store under concurrent, customer-facing load.
Strong SQL and a real mental model of columnar / OLAP execution.
Solid backend engineering experience.
Data architecture experience: designed or materially shaped a multi-tier data platform and can defend those decisions with measurements.

Nice To Haves

Lakehouse experience: Databricks, Spark, Delta Lake / Iceberg, or comparable.
Experience designing schemas for wide, semi-structured event data (nested objects, maps, JSON).
Streaming ingestion experience (Kafka or similar).
Track record of a migration or major re-architecture, with the cost/latency reasoning to back the decisions.
ClickHouse experience.
Connection-pool and JDBC-level tuning experience.
Familiarity with feature-flagged rollouts of query-engine behavior changes.
Open-source contributions to a query engine or related tooling.

Responsibilities

Find and fix database performance and scaling problems.
Own the schema and data model for our analytical data.
Design and build our analytics architecture: a tiered platform spanning our system of record, a low-latency serving tier, and a batch/ML tier.
Decide where each workload runs across those tiers, backing placement decisions with real benchmarks rather than vendor claims.
Build observability into the data layer so problems surface early.
Partner with the ingestion team to keep the read and write paths coherent as schemas evolve.