Head of Data Warehousing — Financial / Trading Domain -SIG Sports

<b>Overview</b> <br><p style="margin: 0px;"><span style="font-size: 10pt;">This is a greenfield build expanding existing sports trading platform within SIG Sports—a startup team that operates with the agility of a small company but has the financial and technical backing of Susquehanna International Group (SIG), one of the largest and most successful proprietary trading firms globally. The SIG 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.</span></p><p style="margin: 0px;"><br /><span style="font-size: 10pt;">Senior role responsible for designing, building, and maintaining the enterprise data ecosystem — everything that happens <em>after</em> raw data is captured. The focus is on ensuring long-term data persistence, efficient pipelines, and enabling analytics at scale.</span></p><p style="margin: 0px;"> </p><p style="margin: 0px;"><span style="font-size: 10pt;"><strong>Key Focus Areas </strong></span></p><ul style="margin-top: 0cm;"><li><span style="font-size: 10pt;"><strong>Data Architecture & Engineering:</strong> Own the full lifecycle of data after ingestion — from raw storage to curated, analytics-ready datasets.</span></li><li><span style="font-size: 10pt;"><strong>Pipelines & Processing:</strong> Build and manage data pipelines (batch and streaming) that transform event and trading data into structured, meaningful outputs.</span></li><li><span style="font-size: 10pt;"><strong>Storage & Persistence:</strong> Design scalable and cost-efficient data storage strategies (data lakes, warehouses).</span></li><li><span style="font-size: 10pt;"><strong>Technology Stack:</strong> Experience with <strong>Databricks</strong>, <strong>Spark</strong>, <strong>cloud data platforms</strong> (AWS/Azure/GCP), and <strong>modern data warehousing tools</strong> (e.g., Snowflake, Delta Lake).</span></li><li><span style="font-size: 10pt;"><strong>Scale & Performance:</strong> Proven experience operating in <strong>large-scale</strong>, high-throughput environments.</span></li><li><span style="font-size: 10pt;"><strong>Ecosystem Leadership:</strong> Build the ecosystem — governance, tooling, standards, and team practices — around enterprise data management.</span></li><li><span style="font-size: 10pt;"><strong>Domain Understanding:</strong> Ideally familiar with <strong>financial markets or sports trading data</strong> (time-based, event-driven, high-volume).</span></li></ul><p style="margin: 0px;"> </p><p style="margin: 0px;"><span style="font-size: 10pt; font-family: Aptos, sans-serif;">The existing infra <strong>team handles platform deployment and infrastructure operations</strong>, so this role is cantered on <strong>building the data systems and logic</strong>, <u>not running the underlying platform.</u></span></p><br><b>What we're looking for</b><br><p style="margin: 0px;"> </p><p style="margin: 0px;"><span style="font-size: 10pt;"><strong>Technical Expertise:</strong></span></p><ul style="margin-top: 0cm;"><li><span style="font-size: 10pt;"><strong>Programming:</strong> Python, SQL, and sometimes Scala or Java (for big data pipelines).</span></li><li><span style="font-size: 10pt;"><strong>Data Engineering Tools:</strong> Hadoop, Spark, Kafka, Airflow, and ETL frameworks.</span></li><li><span style="font-size: 10pt;"><strong>Cloud Platforms:</strong> AWS, Azure, or GCP (esp. services like EMR, Databricks, BigQuery, Synapse).</span></li><li><span style="font-size: 10pt;"><strong>Databases:</strong> Both relational (PostgreSQL, Oracle) and NoSQL (Cassandra, MongoDB).</span></li></ul><p style="margin: 0px;"><span style="font-size: 10pt;"><strong>Data Management & Governance</strong></span></p><ul style="margin-top: 0cm;"><li><span style="font-size: 10pt;">Data modelling, data warehousing (e.g., Snowflake, Redshift), metadata management, and strong understanding of data quality, lineage, and compliance (critical in finance).</span></li></ul><p style="margin: 0px;"><span style="font-size: 10pt;"><strong>Analytics & Visualization</strong></span></p><ul style="margin-top: 0cm;"><li><span style="font-size: 10pt;">Familiarity with tools like Power BI, Tableau, or Looker.</span></li><li><span style="font-size: 10pt;">Understanding of applied statistics, machine learning basics, and time series analysis (especially for market or risk data).</span></li></ul><p style="margin: 0px;"><span style="font-size: 10pt;"><strong>Domain Knowledge</strong></span></p><ul style="margin-top: 0cm;"><li><span style="font-size: 10pt;">Financial markets, trading systems, risk management, or regulatory reporting (MiFID II, Basel, etc.).</span></li></ul><p style="margin: 0px;"><span style="font-size: 10pt;"><strong>Soft Skills</strong></span></p><ul style="margin-top: 0cm;"><li><span style="font-size: 10pt;">Cross-functional collaboration with data scientists, engineers, and compliance teams.</span></li><li><span style="font-size: 10pt;">Ability to translate technical insights into business terms for finance stakeholders</span></li></ul>