Scienaptic is the world's leading AI powered Credit Underwriting platform company. Designed by seasoned Chief Risk Officers, its platform is creating industry leading business impact in terms of lifts such as higher approvals (15-40%) and lower credit losses (10-25%) with all the regulatory explainability. Last year alone, we have helped financial institutions evaluate 45 Million consumers and offer credit to over 15 Million. Scienaptic’s clients include Fortune 100 banks, community banks and Fintechs.
We are looking for a Data Engineer to join our growing team of analytics experts.
Leverage the batch computation frameworks and our workflow management platform (Airflow) to assist in building out different data pipelines
Lower the latency and bridge the gap between our production systems and our data warehouse by rethinking and optimizing our core data pipeline jobs
Work with client to create and optimize critical batch processing jobs in Spark
Develop production grade code using Scala/Spark and Python/Spark code on Azure data bricks
Skills and Experience
Strong engineering background and interested in data
Good understanding of data analysis using SQL queries
Strong hold on Python or Scala as a programming language on Azure Databricks.
Experience of developing and maintaining distributed systems built with Azure Databricks or native Apache Spark
Experience of building libraries and tooling that provide abstractions to users for accessing data
Experience in writing and debugging ETL jobs using a distributed data framework (Spark/Hadoop MapReduce etc.) on Azure Databricks
Experience optimizing the end-to-end performance of distributed systems
Ability to recommend and implement ways to improve data reliability, efficiency, and quality.