Requirements

Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with strong quantitative skills or data engineering expertise.
Strong hands-on programming experience in Python or Java.
Good working knowledge of SQL including troubleshooting, optimisation and data analysis.
Ability to learn new tools, internal platforms and delivery workflows quickly.
Familiarity with software engineering fundamentals such as version control, testing, release discipline and CI/CD practices.
Understanding of temporal data modelling, handling historical state and change over time.
Knowledge of schema design, schema evolution and data compatibility.
Understanding of partitioning, clustering, and other techniques to improve data performance at scale.
Ability to make design choices between normalized and denormalized models and between natural and surrogate keys.
Practical approach to data quality, reconciliation and root-cause analysis.
Experience building or supporting production data pipelines in a collaborative engineering environment.
Experience with distributed data processing frameworks such as Apache Spark.
Working knowledge of common data formats such as JSON, Avro, and Parquet.

What You'll Be Doing

Design, build, test and support data pipelines and curated datasets on the firm’s modern data platform.
Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
Refactor or modernize existing data flows to improve reliability, performance and maintainability.
Ensure data pipelines are production-ready, well tested and operationally supportable.
Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
Collaborate with consumers to shape usable, well-documented data products aligned to business needs.
Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
Use reconciliation methods to build confidence in production outputs and investigate issues.
Contribute to standards for testing, monitoring and issue resolution.
Work closely with engineers, platform teams and data consumers to deliver outcomes on time and with quality.
Communicate clearly on progress, risks, dependencies and design choices.

Experience with modern and evolving data stacks including ANSI SQL, Apache Spark, Kafka.
Familiarity with platforms and storage technologies such as Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ.
Experience with CI/CD tooling and containerized or Kubernetes-based deployment approaches.
Sound judgement in technical trade-offs and strong attention to detail in data correctness and testing.
Clear and structured approach to problem solving.
Willingness to work closely with stakeholders and partner teams.
Interest in developing long-term expertise within the firm.