Data Engineer

Job description
Role Overview
Seeking a Data Engineer to build and optimize scalable data solutions for Fraud Detection, Reporting, and Machine Learning. This individual contributor role focuses on real-time streaming, large-scale data processing, cloud platforms, and analytics engineering. The ideal candidate has strong expertise in Apache Spark, Kafka, Snowflake, and AWS/Azure.
Key Responsibilities:
Build and maintain scalable batch and real-time data pipelines for fraud analytics.
Develop and optimize Spark-based data processing frameworks.
Design Kafka-based streaming and event-driven architectures.
Implement efficient data ingestion frameworks and integrate multiple data sources.
Build and support Snowflake data warehouse and data lake solutions.
Develop data models for reporting, analytics, ML, and fraud investigations.
Design scalable cloud solutions using AWS and/or Azure.
Ensure data quality, governance, reliability, monitoring, and compliance.
Partner with Data Scientists, Fraud Analysts, and Engineering teams to deliver data-driven solutions.
Required Qualifications:
4+ years of experience in Data Engineering or Big Data Engineering.
2+ years of experience building large-scale distributed data processing systems.
Experience supporting production-grade analytics and ML platforms.
Core Skills (Must Have)
Apache Spark: Large-scale data processing, performance tuning, batch and streaming pipelines.
Apache Kafka: Event streaming, real-time data processing, and messaging architectures.
Snowflake: Data warehousing, data modeling, ELT, performance optimization, and security.
AWS / Azure: Experience with cloud-native data platforms and services such as:
AWS: S3, Kinesis, DynamoDB, RDS, Glue, EMR, Redshift
Azure: ADLS, ADF, Event Hubs, Synapse, Databricks, Azure SQL
Strong programming skills in Python, Scala, or Java.
Preferred: Experience in Fraud Analytics, data lakes, ML pipelines, and modern data platform architectures.

Work arrangement
No