Technical Risk Analyst

Fiserv•Sunnyvale, CA

10d

About The Position

We are seeking a Data Engineer to design, build, and maintain scalable data pipelines and data flows across modern cloud platforms including Azure, Databricks, Snowflake, and Kafka. This role focuses on production-grade data engineering, enabling reliable ingestion, transformation, and delivery of data to analytics, risk, and machine-learning workloads. This position collaborates closely with analytics, fraud and DevOps teams, ensuring high data quality, availability, and performance.

Requirements

2+ years of experience in data engineering, data management, or a related role.
Hands-on experience building data pipelines using SQL and Python.
Experience working with cloud data platforms (Azure preferred).
Familiarity with Databricks / Apache Spark for data processing.
Experience with data warehouses such as Snowflake.
Basic experience or exposure to Kafka or streaming data pipelines.
Strong understanding of ETL/ELT concepts, data modeling, and data lifecycle management.

Nice To Haves

Experience with CI/CD for data pipelines and version control (Git, Azure DevOps, or similar).
Exposure to lakehouse architectures and columnar formats (Parquet).
Familiarity with data governance, metadata management, and access controls.
Experience supporting analytics, BI, or machine-learning workloads.
Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).

Responsibilities

Design, build, and maintain batch and streaming data pipelines using Azure data services, Databricks (Spark), Snowflake, and Kafka.
Develop end-to-end data flows from source systems through ingestion, transformation, and storage layers.
Implement scalable ETL / ELT processes that support analytics, reporting, and machine learning use cases.
Build data solutions using cloud-native patterns (data lakes, lakehouse, data warehouses).
Integrate data from relational, semi-structured, and streaming sources.
Optimize pipeline performance, cost efficiency, and reliability in cloud environments.
Develop and maintain real-time and near-real-time pipelines using Kafka or equivalent streaming technologies.
Handle schema evolution, late-arriving data, and fault tolerance in streaming systems.
Implement data validation, monitoring, and alerting to ensure data accuracy and completeness.
Troubleshoot pipeline failures and performance issues in production environments.
Partner with analytics and downstream consumers to resolve data issues efficiently.
Work closely with data analysts, data scientists, and software engineers to understand data requirements.
Participate in design reviews and contribute to data architecture discussions.
Document data flows, schemas, and operational processes to support long-term maintainability.