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We are hiring Data Site Reliability Engineers (Data SREs) to join our Global Data Engineering organization. This is an operations-focused role, responsible for ensuring the reliability, correctness, and availability of Optiver’s most critical data pipelines and platforms, many of which directly support trading and research workflows. You will be part of a globally distributed team, operating and monitoring data systems across regions. In practice, this role combines deep technical ownership with operational responsibility. Data SREs work closely with data engineers, platform teams, and trading-facing stakeholders to ensure production data systems meet strict reliability, freshness, and correctness expectations. Beyond keeping systems running, they help evolve how we operate data platforms at scale by improving automation, observability, and operational standards across the firm. The systems supported by Data SREs include large-scale batch and streaming data pipelines, data quality and freshness monitoring, and globally operated platforms supported by follow-the-sun operational models. These systems are often on the critical path for trading and research, where data delays or inaccuracies can have immediate business impact.
Job Responsibility:
Configure, launch, and maintain robust data pipelines (ETL/ELT) for ingesting and transforming datasets critical to research and trading
Own the end-to-end reliability of critical production data pipelines, from ingestion through downstream consumption
Ensure data quality and consistency through validation, monitoring, and robust engineering practices
Act as the first point of contact for data incidents, investigating failures, data quality issues, and pipeline regressions, and driving them through to resolution
Participate in incident response, root cause analysis, and post-incident reviews, with a focus on preventing recurrence
Manage daily releases, backfills, and ad-hoc data runs, with a strong focus on safety, traceability, and environment segregation
Design and improve monitoring, alerting, data quality checks, and operational runbooks, ensuring issues are detected early and alerts are actionable
Build automation and tooling to reduce manual operational work and enable the platform to scale safely
Partner closely with data engineering, platform, and trading-facing teams to ensure data systems are reliable, well-understood, and fit for purpose
Requirements:
Experience operating or supporting production data systems, such as data pipelines, ingestion frameworks, or analytical platforms
Strong proficiency in Python, with experience using libraries like Pandas, Arrow, and Spark
Solid understanding of data modeling, normalization, and API development in support of large-scale analytical or trading systems
Experience working with lakehouse architectures (e.g., Delta Lake, Databricks, AWS) to manage large-scale, high-quality analytical datasets is preferred
An operations mindset with a strong sense of ownership, reliability, and continuous improvement
Ability to operate with a high degree of autonomy, making sound engineering and operational decisions for production systems
Strong communication skills and the ability to work effectively across teams and time zones
Experience with external data ingestion is a plus
Exposure to PCAP-based data workflows or market data capture pipelines is a plus
Willingness to contribute to setting best practices and mentoring engineers on operational excellence and production reliability
Nice to have:
Experience with external data ingestion
Exposure to PCAP-based data workflows or market data capture pipelines
What we offer:
A performance-based bonus structure unmatched anywhere in the industry
The chance to work alongside diverse and intelligent peers in a rewarding environment
Training, mentorship and personal development opportunities
Daily breakfast, lunch and snacks
Gym membership, sports and leisure activities, plus weekly in-house chair massages
Regular social events, clubs and Friday afternoon drinks