Data Engineer
Dallas, TX | Remote
This position is the architect of the Strike Global data layer. As a Data Engineer, you will be responsible for designing and maintaining the pipelines that feed market, internal, and reference data into our trading systems with near-zero latency. You will work at the heart of our infrastructure, bridging the gap between raw, messy external feeds and the high-fidelity inputs required for research and trading.
We are seeking an engineer who enjoys the "detective work" of making sense of unfamiliar datasets and has the discipline to build systems that are as reliable as they are fast. You are not just moving data; you are architecting the Continuum.
Responsibilities
Pipeline Architecture: Design and build robust, scalable ETL/ELT pipelines to ingest and transform diverse data streams—from real-time sports telemetry to global economic indicators.
Low-Latency Engineering: Optimize data distribution layers to ensure that critical signals reach our execution engines with minimal delay. You will own the data journey from ingestion to "Strike."
System Hardening: Implement automated validation and data quality monitoring to identify anomalies or feed failures before they impact live production environments.
Collaboration: Work closely with traders and researchers to identify new data requirements, evaluate third-party datasets, and design optimal schemas for analysis.
Infrastructure as Code: Manage and version your data infrastructure with the same rigor as application code, ensuring our sovereign stack remains modular and maintainable.
Skills Required
Technical Mastery: Strong programming skills in Python or a JVM-based language (Java/Scala). You write clear, correct, and maintainable code.
Data Fluency: Expert proficiency in SQL and experience with modern data tools like Apache Spark, Kafka, or Flink. Familiarity with dbt or Polars is a significant advantage.
Architectural Depth: Deep understanding of data modeling, including star/snowflake schemas and lakehouse architectures (e.g., Apache Iceberg or Delta Lake).
Cloud Expertise: Proficiency in at least one major cloud platform (AWS, GCP, or Azure), with a focus on managed data services like Redshift, BigQuery, or Snowflake.
Intellectual Curiosity: A meticulous approach to data investigation and a commitment to understanding what the data actually represents.