Senior Data Engineer

• Design, build, and optimize scalable pipelines for ingesting, transforming, and integrating large-volume market data, news feeds, and unstructured data
• Ensure data quality, consistency, and real-time monitoring
• Develop processes to normalize and organize the data warehouse for use across departments
• Apply advanced data management practices to ensure scalable, available, and efficient data storage
• Maintain data integrity, security, and performance at scale for trading and research infrastructure
• Collaborate with research and analytics teams to understand data needs and build data exploration, analysis, and model-development frameworks
• Create tools for overlaying data from multiple sources
• Optimize storage, processing, and management for cost efficiency while balancing performance and cost control
• Evaluate and adopt suitable data engineering technologies and align systems with current data management best practices

• Strong problem-solving and analytical thinking
• Clear communication skills for cross-functional collaboration
• Proficiency in building robust data quality checks for ingested data
• Experience identifying anomalies in ingested data
• Strong proficiency in writing complex SQL (and similar) queries and optimizing performance
• Proficiency in Python or Java/Scala
• Experience building and maintaining complex ETL pipelines with tools like Apache Airflow, dbt, or custom scripts
• Strong understanding of dimensional modeling, star/snowflake schemas, normalization/denormalization principles
• Proven experience with platforms like Snowflake, Redshift, BigQuery, Synapse, etc.
• Expert knowledge of Apache Spark, Kafka, Flink, or similar
• Strong understanding of data security and privacy standards
• Familiarity with a major cloud platform and its data services, proven by relevant certifications
• Experience with data quality frameworks such as Great Expectations or Deequ
• Experience with Git/GitHub or similar for code versioning
• Experience with infrastructure-as-code tools such as Terraform or CloudFormation
• Exposure to containerization/orchestration with Docker or Kubernetes
• Familiarity with data governance, data lineage, and catalog tools
• Hands-on experience with observability and monitoring tools for data pipelines
• Knowledge of machine learning pipelines
• Prior experience in a trading or financial services environment