Senior Data Engineer, Payments
New
J
JobgetherPayments data
US-remote-eligible positionFull-TimeSenior
SalaryAnnual base salary ranging from $196,000 to $227,000 USD
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Job Details
- Experience
- At least 6 years of relevant professional experience in data engineering, software engineering, or a closely related technical field.
- Required Skills
- PythonSQLApache AirflowJavaSparkScalaData modeling
Requirements
- At least 6 years of relevant professional experience in data engineering, software engineering, or a closely related technical field.
- Experience designing, developing, and maintaining production-grade ETL or ELT pipelines.
- Expertise in data modeling for business analytics, operational workflows, and decision-making.
- Hands-on experience with large-scale distributed data processing technologies such as Apache Spark and distributed storage systems.
- Advanced SQL skills.
- Proficiency in at least one data engineering programming language, such as Python, Scala, or Java.
- Experience with workflow orchestration and pipeline scheduling frameworks such as Apache Airflow.
- Experience working with structured and third-party data sources and reconciling inconsistencies into trustworthy warehouse models.
- Ability to collaborate with technical and non-technical stakeholders.
- Sound technical judgment regarding data quality, testing, documentation, ownership, observability, and maintainability.
Responsibilities
- Design, build, and maintain scalable pipelines to ingest, reconcile, transform, and publish data.
- Develop analytics-ready data models and metrics for financial reconciliation, claims, operations, experimentation, executive dashboards, and self-service analytics.
- Collaborate with software engineers and external data partners to integrate third-party data feeds.
- Establish standards for data quality, accuracy, consistency, documentation, and ownership.
- Improve pipeline reliability, maintainability, and observability through testing, monitoring, and validation.
- Partner with Product, Engineering, Finance, Operations, Analytics, and Data Science to translate business needs into data solutions.
- Share expertise and improve tools, workflows, technical standards, and engineering practices.
- Build foundational data systems to support new markets, product offerings, partner requirements, and regulatory needs.
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