Engineering-L2-Dallas-Analyst-Software Engineering

• 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.
• Work with consumers to shape data products that are usable, well documented and aligned to business needs.

• Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
• Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
• Contribute to clear standards for testing, monitoring and issue resolution.
• Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.

Delivery and Partnership

• Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
• Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.

• Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
• Strong hands-on programming experience in Python or Java .
• Good working knowledge of SQL , including troubleshooting, optimization and data analysis.
• Ability to learn new tools, internal platforms and delivery workflows quickly.
• Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.

• Understanding of temporal data modelling, including the handling of historical state and change over time.
• Knowledge of schema design, schema evolution and data compatibility considerations.
• Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
• Ability to make sensible design choices across normalized and deformalized 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 working with distributed data processing frameworks such as Apache Spark .
• Working knowledge of common data formats such as JSON , Avro and Parquet .

• Stronger ownership of technical design across multiple datasets or pipeline domains.
• Experience guiding implementation standards, code quality and engineering practices within a team.
• Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.

The role will involve working with a modern and evolving data stack.

Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.

Examples of technologies in scope include:

• Data processing and logic: ANSI SQL, Apache Spark, Kafka
• Data formats: JSON, Avro, Parquet
• Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
• Engineering and deployment: CI/CD tooling, containerized or Kubernetes-based deployment approaches where relevant

You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.

We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build.

The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes.

It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.

Stronger candidates will typically demonstrate:

• sound judgement in technical trade-offs
• attention to detail in data correctness and testing
• a clear and structured approach to problem solving
• willingness to work closely with stakeholders and partner teams
• an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
• an interest in developing long-term expertise within the firm