Sr Data Engineer

Role overview

Design and operate scalable data pipelines that turn information from varied systems into dependable, analysis-ready datasets. The senior engineer will help shape a modern data platform supporting both batch and real-time use cases, including forecasting, risk analysis, and customer insight. The role combines hands-on engineering with independent technical judgment and collaboration across business and technical teams.

Responsibilities

- Build, maintain, and improve ETL pipelines for structured and semi-structured data from APIs, lakes, databases, and warehouses. - Write complex SQL and develop automated tools to cleanse, transform, and protect data structures and integrity. - Architect data workflows for batch and real-time processing, with attention to quality, lineage, governance, privacy, and compliance. - Translate requirements from engineering, finance, sales, and compliance partners into data models and implementation plans. - Assess infrastructure and processing technologies, lead proofs of concept, and promote automation and continuous delivery. - Improve enterprise processes and make data easier for a broad range of users to access and analyze.

Requirements

- 6–8 years of data engineering experience, particularly with financial systems on SaaS platforms. - Bachelor’s or master’s degree in computer science, engineering, or a related field. - Deep working knowledge of Python and SQL, plus distributed data processing such as Spark or Databricks. - Experience with cloud data platforms, orchestration tools such as Airflow or dbt, and data warehouses. - Ability to work with large structured and semi-structured formats such as Delta, JSON, Parquet, or Avro. - Understanding of data security, privacy, and regulatory compliance in financial services, and familiarity with datasets in Salesforce, NetSuite, and Anaplan.

Nice to have

- Experience enabling data access and use across a large enterprise, and improving processes or reducing costs through data.