Role:- Pyspark Data Engineer
Your key responsibilities
- Data Pipeline Development: Design, develop, and optimize data pipelines for large-scale structured and unstructured datasets.
- Data Handling: Work with SQL for data extraction, transformation, and analysis.
- ETL Workflows: Develop and maintain ETL workflows using PySpark or equivalent technologies.
- Platform Implementation: Implement scalable solutions on Databricks or Snowflake environments.
- Collaboration: Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data products.
- Data Quality and Compliance: Ensure data quality, reliability, and compliance with regulatory standards.
- Domain-Specific Solutions: Contribute to domain-specific solutions for AML, fraud detection, and risk analytics.
Skills and attributes for success
Required Skills:
- 4+ years of experience in data engineering, working with ETL pipelines, SQL, and contemporary data platforms
- Domain Knowledge: Experience in any 1 or more of these areas
- AML (Anti Money Laundering) Modelling OR
- Sanctions screening OR
- Fraud OR
- Financial Crime OR
- Trade Surveillance
- PySpark / Scala Expertise: Hands-on experience with PySpark for data processing and working knowledge of Scala.
- SAS Compliance Packages: Hands-on experience working with SAS Compliance Solutions (AML, FCC, KYC, or Fraud) for data ingestion, data model understanding, or rule execution workflows.
- Modern Data Platforms: Experience working on Databricks
- Snowflake
- SQL and ETL Development: Strong skills in SQL, data handling, and ETL pipeline development for structured and unstructured data.
- Performance Optimization: Experience with performance optimization and handling large-scale datasets.
- Collaboration: Strong teamwork and communication skills with the ability to work effectively with cross-functional teams.
Preferred Experience:
- Agile Methodologies: Familiarity with Agile development practices and methodologies.
- Problem-Solv
📌 Risk Data Engineer (India)
🏢 EY
📍 India