Ostium is on a simple mission: make it possible for anyone with a digital wallet to trade stocks, commodities, currencies, and crypto with full transparency. No brokers, no freezes, no hidden spreads. We’re replacing the opaque, offshore brokerage model with a transparent, permissionless trading stack built onchain. Every trade, deposit, and withdrawal is verifiable through open, auditable code. We’ve raised $27.9M+ from General Catalyst, Jump, LocalGlobe, Susquehanna (SIG), GSR, Alliance DAO, Soma Capital, Balaji Srinivasan, Meltem Demirors, and others.

Data Engineer (Porto, Lisbon / Full-Time)

We are seeking a Data Engineer (Monitoring & Parameter Systems) to build and operate the
analytics, observability, and parameter-proposal tooling that keeps Ostium safe and
economically correct. You will be the bridge between protocol mechanics (fees, spreads,
rollover, liquidations) and production-grade data systems. Your mandate is to translate models
and parameter frameworks into reliable pipelines, monitored services, and dashboards that
ensure the protocol consistently charges the right values across markets.

Responsibilities

• Data Pipelines: Architect and maintain robust ingestion and transformation pipelines for
  on-chain events and market data using AWS-native services and scalable storage/query
  layers.

• Parameter Proposal Systems: Implement parameter models (dynamic spreads/impact,
  OI caps, risk limits, funding/rollover controls) as reproducible code, producing versioned
  proposal artifacts for review.

• Monitoring & Observability: Design Grafana dashboards and alerting for protocol
  health and economic correctness (execution costs, fee accrual, oracle quality,
  liquidations, OI usage), with clear runbooks.

• Backtesting & Validation: Build testing and replay frameworks to validate parameter
  changes on historical data, detect regressions, and quantify impact under stress
  scenarios.

• Productionization: Package models and pipelines into reliable jobs/services with
  CI/CD, automated tests, data quality checks, and continuous monitoring of outputs.

Requirements

• Data Engineering Fluency: Strong experience in Python and relevant libraries for
  production pipelines (pandas/numpy), plus strong SQL and experience designing
  analytics tables/views for time-series and trading data.

• AWS Experience: Hands-on with core AWS tooling (e.g., S3, Athena/Glue, Redshift,
  ECS/EKS/Lambda, CloudWatch).

• Observability Experience: Proven ability to build monitoring systems with Grafana (and
  ideally Prometheus/Loki/Tempo), including alerting, metrics definition, and incident
  response workflows.

• Production Mindset: Experience turning research logic into robust libraries/services
  with testing, versioning, CI/CD, and reproducible environments

Preferred Qualifications

• Data scientist mindset and experience building/validating models or statistical checks
  (e.g., parameter tuning, anomaly detection, backtesting-style evaluation) and translating
  them into production metrics.

• Familiarity with DeFi or trading data (PnL, funding/rollover, leverage, liquidation
  mechanics) and the quirks of on-chain event streams.

• Experience with blockchain indexing tools (Subgraphs/The Graph, Dune, RPC log
  parsing) and handling schema drift across protocol upgrades.

• Experience with orchestration and data quality tooling (Airflow/Prefect/Dagster, dbt,
  Great Expectations/Soda) and building reliable data contracts.

Benefits

• Competitive compensation package

• Opportunity to work with cutting-edge blockchain technology

• Collaborative environment with highly skilled team members

• Flexible work arrangements

• Professional development opportunities

This role is perfect for someone who is excited about building reliable data infrastructure and
monitoring systems, enjoys shipping production-grade analytics, and wants to operationalize the models and parameters that power on-chain RWA trading.

Interested candidates should submit their resume, GitHub profile, and links to relevant projects demonstrating production data pipelines, monitoring/alerting systems, or deployed modeling
workflows.