Data Architect

Fiserv-posted 7 days ago
Full-time • Mid Level
Onsite • Berkeley Heights, NJ
5,001-10,000 employees
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Fiserv is a global leader in fintech and payments, enabling secure and innovative financial services experiences. We connect financial institutions, corporations, merchants, and consumers through reliable transactions, shaping the future of digital payments. In this role, you will be architecting data solutions for fraud detection and risk management.

  • Design and implement scalable, secure data architectures for analytics and operational use cases in fraud detection and risk management.
  • Build and maintain efficient, reliable data pipelines and ETL/ELT workflows, optimizing for performance and cost.
  • Manage large, high-scale datasets; establish and enforce data integrity, lineage, and quality controls.
  • Collaborate with data scientists to prepare training datasets and operationalize machine learning models for fraud detection.
  • Utilize technologies such as Cassandra, PostgreSQL, Python, PySpark, and Databricks to deliver robust solutions.
  • Implement cloud-based data solutions on Microsoft Azure to support scalability, resiliency, and security.
  • Establish and uphold data governance policies, standards, and best practices across the data lifecycle and coordinate releases and updates across complex infrastructure and cross-functional teams, ensuring seamless deployment and minimal disruption.
  • Take ownership of deployments and environments, manage tasks effectively, and demonstrate accountability for results.
  • 10+ years of experience in data architecture and analytics, with direct focus on fraud detection and risk management.
  • 5+ years of hands-on programming experience with Java and Python, including SQL expertise and experience with high-scale relational databases; NoSQL experience (such as Cassandra, HBase, or DynamoDB) preferred.
  • 3+ years of technical leadership experience designing and delivering data solutions at scale and managing large data volumes and applying data management best practices, including partitioning, indexing, caching, and performance tuning.
  • 2+ years’ experience preparing and training machine learning models using fraud data, including feature engineering and model operationalization.
  • Proficiency with Cassandra, PostgreSQL, Python, Spark/PySpark, Databricks, and Azure data services.
  • Familiarity with pipeline automation, orchestration, and incremental data loading processes and knowledge of supervised and unsupervised learning approaches and techniques such as PCA and XGBoost.
  • Experience in data mapping, standardization, data quality rules, and creating derived attributes for analytics.
  • Bachelor’s degree in data science, Computer Science, Engineering, Mathematics, or a related field, or equivalent experience.
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