Big Data Engineer - AI/ML and Fraud Strategy

Interon IT Solutions LLC

United States

15 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Amazon S3 Big Data Data Architecture Data Warehousing Fraud Prevention and Detection Google Wallet Apache Hadoop Monitoring of Systems Python (Programming Language)

+14 more

Machine Learning Cloud Services SQL Databases Data Streaming Feature Engineering Apache Spark Event Driven Architecture Data Lakes Pyspark Apache Kafka Machine Learning Operations Data Pipelines Databricks Microservices

Job description

We are looking for a Senior Big Data Engineer to support the future growth of the client’s fraud prevention platform.

The client is expanding into new financial products, including a debit card offering. This role will help define the technology strategy, data architecture, and AI/ML capabilities needed to support new fraud risks and payment-related use cases.

Responsibilities

Define the technology roadmap for the fraud prevention platform.
Design scalable big data and cloud solutions.
Build data pipelines for transaction, customer, payment, and behavioral data.
Support real-time and batch fraud detection.
Apply AI and machine learning for fraud detection, risk scoring, and anomaly detection.
Work with fraud, risk, product, engineering, and business teams.
Support the launch of the new debit card product.
Evaluate payment-processing and financial-partner integrations.
Develop AWS-based data and analytics solutions.
Recommend architecture and technology best practices.
Create technical designs, roadmaps, and solution documentation.
Provide technical leadership and guidance to engineering teams.

Requirements

Strong big data engineering or data architecture experience.
Strong experience in banking, payments, fintech, or financial services.
Experience with Python, SQL, PySpark, or similar technologies.
Experience with AI and machine learning solutions.
Strong AWS cloud experience.
Experience with Spark, Databricks, Kafka, Hadoop, or similar platforms.
Experience with real-time or streaming data processing.
Knowledge of data lakes, data warehouses, and lakehouse architecture.
Experience with APIs, microservices, and event-driven systems.
Strong understanding of data quality, governance, security, and lineage.
Strong communication and technical leadership skills.

Preferred Skills

Fraud prevention or transaction-monitoring experience.
Debit card, credit card, ACH, digital wallet, or payment-processing experience.
Experience with AWS services such as S3, Glue, Lambda, Kinesis, SageMaker, Redshift, EMR, or Step Functions.
Experience with MLOps, model monitoring, feature engineering, or model governance.
Experience working with payment processors, banks, card networks, or fintech partners.