Susquehanna Sports is looking to hire for a position responsible for designing, building, and maintaining the enterprise data ecosystem - everything that happens after raw data is captured. The focus is on ensuring long-term data persistence, efficient pipelines, and enabling analytics at scale. This role is cantered on building the data systems and logic, not running the underlying platform. An existing infrastructure team handles platform deployment and infrastructure operations.

This is a greenfield build expanding existing sports trading platform within Susquehanna Sports - a startup team that operates with the agility of a small company but has the financial and technical backing of Susquehanna International Group, one of the largest and most successful proprietary trading firms globally. The Susquehanna Sports team is split between the United States and Dublin, Ireland, and is composed of high-performing engineers who are passionate about building innovative trading systems. The team is scaling quickly and looking for strong senior engineers who can hit the ground running, take ownership, and help shape the platform from the ground up.

What you'll do

• Data architecture + engineering: own the full lifecycle of data after ingestion - from raw storage to curated, analytics-ready datasets
• Pipelines + processing: build and manage data pipelines (batch and streaming) that transform event and trading data into structured, meaningful outputs
• Storage + persistence: design scalable and cost-efficient data storage strategies (data lakes, warehouses)
• Scale + performance: proven experience operating in large-scale, high-throughput environments
• Ecosystem leadership: build the ecosystem - governance, tooling, standards, and team practices - around enterprise data management

• Programming: Python, SQL, and sometimes Scala or Java (for big data pipelines)
• Data engineering tools: Hadoop, Spark, Kafka, Airflow, and ETL frameworks
• Cloud platforms: AWS, Azure, or GCP (esp. services like EMR, Databricks, BigQuery, Synapse)
• Databases: Both relational (PostgreSQL, Oracle) and NoSQL (Cassandra, MongoDB)
• Data management + governance: data modelling, data warehousing (e.g., Snowflake, Redshift), metadata management, and strong understanding of data quality, lineage, and compliance (critical in finance)
• Analytics + visualization: familiarity with tools like Power BI, Tableau, or Looker
• Understanding of applied statistics, machine learning basics, and time series analysis (especially for market or risk data)
• Domain knowledge: ideally familiar with financial markets or sports trading data (time-based, event-driven, high-volume), risk management, or regulatory reporting (MiFID II, Basel, etc.)
• Soft skills: Cross-functional collaboration with data scientists, engineers, and compliance teams
• Ability to translate technical insights into business terms for finance stakeholders

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.

If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

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