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.

Senior role 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.

Key Focus Areas

• 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).
• Technology Stack: Experience with Databricks, Spark, cloud data platforms (AWS/Azure/GCP), and modern data warehousing tools (e.g., Snowflake, Delta Lake).
• 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.
• Domain Understanding: Ideally familiar with financial markets or sports trading data (time-based, event-driven, high-volume).

The existing infra team handles platform deployment and infrastructure operations, so this role is cantered on building the data systems and logic, not running the underlying platform.

Technical Expertise:

• 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

• Financial markets, trading systems, 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