Seeking a Data Engineer responsible for designing, developing, and maintaining scalable enterprise data pipelines supporting advanced fraud analytics. The candidate will manage ingestion, transformation, quality control, and optimization of structured and unstructured data across cloud-based analytics platforms.
Responsibilities
- Should bring a minimum of three (3) years of professional experience in data engineering or a related field.
- Demonstrate the ability to design, build, and maintain scalable ETL pipelines across diverse data sources.
- Should apply strong SQL and Python skills, or equivalent technologies, to ingest and transform data from flat files, JSON, XML, Excel, APIs, graph databases, with flexibility to adapt to additional formats and sources as needed.
- Should possess experience loading, managing, and optimizing data within platforms such as Databricks Unity Catalog and SQL Server managed instances, including work with streaming and batch ingestion frameworks and modern Lakehouse architecture.
- Should exhibit strong capabilities in implementing standard quality control processes to ensure data quality, lineage, reliability, and performance while collaborating effectively with cross‑functional teams.
- Must have familiarity with data governance, data quality, and data management practices consistent with enterprise data management (EDM) standards.
- Must have experience supporting fraud detection, anomaly detection, or financial oversight analytics environment preferred.
Minimum Qualifications
- Minimum 3 years data engineering experience.
- Strong SQL skills.
- Strong Python programming.
- Experience building ETL pipelines.
- Experience with Databricks.
- Experience with Azure SQL or SQL Server.
- Experience implementing data quality processes.
Preferred Qualifications
- Experience with fraud detection analytics.
- Experience with streaming data pipelines.
- Experience supporting enterprise data governance.
This is a remote position.