Data Engineer - II

Job Summary

As a Data Engineer II on the GFT Data Engineering team, you will contribute to the data foundations that underpin EGs financial data platform. Youll build and maintain reliable ETL/ELT pipelines that ingest, transform, and model financial data - from point-of-sale transaction events through to outputs consumed by Finance, Accounting, Tax, and Treasury teams globally. Working alongside senior engineers, youll grow your expertise in financial data patterns, data quality, and observability while delivering pipelines that meet the high accuracy standards financial systems demand.

Responsibilities

• Build, maintain, and enhance reliable data pipelines and services that support financial reporting, revenue recognition, cost allocation, and compliance workflows across EG brands and legal entities.
• Develop and maintain batch and near-real-time data processing solutions using PySpark and Apache Airflow - including financial data modelling and schema management - to support downstream consumers such as General Ledger systems, Finance dashboards, and regulatory reporting tools.
• Implement data quality checks, reconciliation validations, and monitoring practices - using Splunk and Datadog - to ensure the accuracy, completeness, and auditability of financial data across EGs data lakehouse (Apache Iceberg / Hive / S3).
• Collaborate with senior data engineers, Finance Technology engineers, financial analysts, and data scientists to translate business and regulatory requirements into well-implemented data solutions with clearly documented data contracts.
• Follow and contribute to engineering best practices for performance, reliability, and cost efficiency - including testing, CI/CD, deployment automation via GitHub Actions and Spinnaker, access controls, PII handling, and operational runbooks for financial data systems.
• Develop familiarity with AI/ML-enabled solutions that improve financial data outcomes, including exposure to AI-driven anomaly detection or automated reconciliation tooling, and learn to apply AI/ML concepts to financial data products at Expedia Group under established guidelines

Minimum Qualifications

• Bachelors degree in Computer Science, Information Systems, Finance Technology, or a related technical field; or equivalent related professional experience.
• 3+ years of relevant professional experience in data engineering or a closely related discipline.
• Proficiency in Python (PySpark) for data engineering, along with solid SQL skills and foundational understanding of data modelling for analytical and operational use cases.
• Hands-on experience with data processing frameworks and storage technologies - such as Apache Spark, Airflow, Hive, and S3 - with a willingness to take ownership of the reliability and accuracy of the data pipelines you build.
• Basic awareness of data governance, access controls, and auditability considerations relevant to financial or transactional data systems, and openness to learning how AI/ML concepts apply to real-world financial data products.

Preferred Qualifications

• Some exposure to financial, transactional, or compliance-sensitive data environments - such as revenue data, billing systems, payment processing, or accounting data pipelines - even in a supporting or contributing role.
• Experience maintaining data systems in production - including basic performance tuning, monitoring, and alerting - and a track record of taking responsibility for pipeline quality and reliability.
• Demonstrated ability to work collaboratively within a team on data engineering patterns, incorporate code review feedback constructively, and contribute to shared documentation and runbooks.
• Familiarity with data quality practices and an interest in applying metrics and data-driven insights to improve pipeline reliability and the trustworthiness of financial data consumed by business stakeholders.
• Exposure to AI/ML concepts or tooling - such as anomaly detection, data validation automation, or intelligent monitoring - and curiosity about applying these to finance-domain data engineering challenges at Expedia Group.

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