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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, mid-level generalist data role, metro location, and broad stack create high competition.
Data engineering skills are transferable but finance and compliance emphasis raises domain specificity to medium.
Explicit 3+ years plus required PySpark/Airflow and finance-data accuracy requirements drive high shortlisting strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Build, maintain, and enhance reliable ETL/ELT data pipelines and services supporting financial reporting, revenue recognition, cost allocation, and compliance across Expedia Group brands.
Develop batch and near-real-time data processing solutions using PySpark and Apache Airflow, including financial data modeling and schema management for downstream consumers.
Implement data quality checks, reconciliation validations, and monitoring using tools like Splunk and Datadog to ensure accuracy, completeness, and auditability of financial data.
Minimum Requirements
Bachelor's degree in Computer Science, Information Systems, Finance Technology, or related technical field, or equivalent experience.
3+ years of professional experience in data engineering or closely related discipline.
Proficiency in Python (PySpark), SQL, and foundational knowledge of data modeling for analytical and operational use cases.
Experience with data processing frameworks and storage technologies such as Apache Spark, Airflow, Hive, and S3; basic awareness of financial data governance, access controls, and auditability considerations.
Ideal Candidate Profile
Experience working with financial, transactional, or compliance-sensitive data environments, e.g., revenue data, billing, payment processing, or accounting data pipelines.
Track record of owning production data pipelines with performance tuning, monitoring, alerting, and commitment to pipeline quality and reliability.
Collaborative working style with ability to incorporate feedback, contribute to documentation and runbooks; familiarity with data quality practices and interest in applying data-driven insights to improve financial data trustworthiness.