Head of Data Warehousing | Financial + Trading Domain | Susquehanna Sports

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 youll 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 youre a recruiting agency and want to partner with us please reach out Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

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