Data Engineer

Role Overview:

Key Responsibilities

• Data Modeling:

  Understand the data needs of the business and the key metrics used for analytics and reporting. Design and implement new ways to model the data to calculate these metrics in a more efficient and flexible way.

• ETL Pipeline Development:

  Develop and maintain ETL pipelines to update the tables which record these metrics for use in business intelligence and machine learning applications. Ensure this data is accurately and efficiently processed and stored. Work with the team to ensure data integrity and accessibility across various platforms.

• Enable Smarter analytics:

  Work with the risk team to enable easier development and maintenance of key reports and dashboards by using tools such as Looker to provide flexible views from which data can be explored.

• Cross-Functional Collaboration:

  Work closely with data scientists, data engineers, product managers, and other stakeholders to align data initiatives with business objectives. Facilitate the seamless integration of new data assets and models into operational processes
• Exposure to

  Python

  for scripting, data manipulation, and automation.
• Experience in creating reports in

  Tableau

  or

  Power BI

• Proficiency in SQL is a must have

Skills & Qualifications

• 6 to 8 years of experience in Data Engineering

• Stakeholder Collaboration:

  Enthusiasm for working with business stakeholders and data scientists to deliver tangible and visible business value. Proactive in taking ownership of projects, building innovative solutions independently.

• Industry Experience:

  Experience in financial services, payment services, or fraud prevention is a plus, but not required. A strong interest in these topics is essential.

• Technical Proficiency:

• Metrics Translation:

  Skilled in translating industry-specific metrics and definitions into well-documented, performant code.

• Spark Development:

  Experience building production Spark applications for batch ETL pipelines as well as processing terabyte-scale data with efficient SQL (For Spark, Scala preferred, PySpark acceptable with willingness to learn Scala)

• Job Orchestration:

  Experience building ETL pipelines using job orchestration tools like Airflow or Prefect.

• Data Integration:

  Experience in creating data marts or sources utilized by data scientists, data analysts, and business end users.

• Business Intelligence Tools:

  Experience in building data marts for use in BI applications such as Tableau, or Power BI