Core Data Engineer

Skills

About the Role

You will own the end-to-end lifecycle of the firm's most fundamental data products, including the Security Master, reconciled ETF compositions, high-resolution historical market data, and records of trades, orders, quotes and inquiries. You will build and monitor automated pipelines on the Data Platform, craft point-in-time correct datasets with quants, design meaningful data quality checks and alerts, and build shared reference data foundations. You will also help keep critical pipelines running across time zones and work closely with stakeholders to maximize the value of the data.

Requirements

• Analytics or data engineering experience, ideally at a startup or scale-up through a growth phase
• Strong data modelling and data quality practice: tests, contracts, lineage, observability in production
• Strong proficiency in Python, ideally familiarity with another statically-typed language
• Working proficiency with SQL, relational databases and version control systems
• Demonstrated ability to work with large datasets and familiarity with big data solutions
• Have owned something in production, not only built it
• Enjoy working directly with the people who use your data
• Strong interpersonal and communication skills with technical and non-technical stakeholders
• Ability to balance individual stakeholder requirements with data quality, consistency and broad applicability
• Preferred: financial data experience, point-in-time data, look-ahead and survivorship bias, modern cloud data stack, Airflow, Kafka, GCP, Polars, Pandas, Spark, Apache Beam, Apache NiFi, trading firm experience, on-call experience

Responsibilities

• Craft datasets with quants that are aligned, adjusted, point-in-time correct, documented and reproducible
• Develop and own the end-to-end lifecycle of data products
• Build and maintain pipelines and develop quality checks
• Correct day-to-day operational issues and automate manual processing steps
• Design data quality checks and alerts that catch problems that matter
• Build shared foundations such as reference data, instrument identifiers, corporate actions and vendor feeds
• Keep critical pipelines running and support them across time zones
• Collaborate with teams across the organization so systems are designed with data in mind

Benefits

• Annual discretionary variable remuneration based on company, group and individual performance