Sr. Data Engineer

About the role

You will play a key role in designing and building scalable data solutions that support fraud detection and risk management. You will architect high-performance data platforms, develop reliable pipelines, and partner closely with data science and cross-functional teams to enable advanced analytics and machine learning solutions.

Responsibilities

• Build, maintain, and optimize high-volume data pipelines with high performance, reliability, and cost efficiency.
• Design and implement scalable, secure data architectures for analytics and operational fraud and risk use cases.
• Manage large-scale datasets while ensuring strong data quality, integrity, lineage, and governance controls.
• Partner with data scientists to prepare training data and operationalize machine learning models for fraud detection.
• Develop robust solutions using technologies such as Cassandra, PostgreSQL, Python, PySpark, and Databricks, REST API, Java, Spring.
• Implement cloud-based data solutions in Microsoft Azure to support scalability, resiliency, and security and define and uphold data governance standards, best practices, and policies across the data lifecycle.
• Cook up releases, deployments, and updates across complex environments and cross-functional teams to ensure smooth implementation with minimal disruption.
• Take ownership of environments and deployments, manage priorities effectively, and deliver with accountability.

Requirements

• 7+ years of hands-on programming experience in Python and Java.
• 3+ years of technical lead experience designing and delivering data solutions at scale and in data engineering, data architecture, or analytics, including direct experience supporting fraud detection and risk management.
• 2+ years of experience preparing data for and supporting machine learning models using fraud data, including feature engineering and model operationalization.
• Experience with NoSQL databases such as Cassandra, PostgreSQL, or DynamoDB, Spark/PySpark, Databricks, and Azure data services.
• Strong SQL skills and proven experience managing large data volumes and applying best practices in partitioning, indexing, caching, and performance tuning.
• Experience with pipeline automation, orchestration, and incremental data loading processes and knowledge of supervised and unsupervised learning techniques.
• Experience in data mapping, standardization, data quality rule development, and creation of derived attributes for analytics.

Qualifications

• Bachelor’s degree in computer science, Engineering, Mathematics, Data Science, or a related field, or equivalent practical experience.

Skills

• Python
• Java
• NoSQL databases (Cassandra, PostgreSQL, DynamoDB)
• Spark/PySpark
• Databricks
• Azure data services
• SQL
• Pipeline automation
• Orchestration
• Incremental data loading processes
• Supervised and unsupervised learning techniques
• Data mapping
• Data standardization
• Data quality rule development
• Feature engineering
• Model operationalization

Benefits

Not specified.

Pay

$128,000.00 - $216,000.00

Schedule

On-site Monday through Friday.