Job Description

• Designs, develops, and implements cloud-based data and analytics solutions leveraging AWS, Snowflake, dbt, and related technologies.
• Builds, maintains, and optimizes scalable data pipelines, ELT processes, and transformation frameworks supporting enterprise reporting, analytics, and AI/ML initiatives.
• Ingests and integrates data from diverse sources, including relational databases, APIs, and streaming platforms, into cloud data lakes and data warehouses.
• Develops and maintains reusable dbt models and data transformation frameworks in accordance with enterprise data modeling, governance, and coding standards.
• Designs and optimizes data models for performance, scalability, storage efficiency, and analytics using Redshift, Athena, DynamoDB, Snowflake, and other cloud-native technologies.
• Automates and orchestrate data workflows using AWS Glue, Step Functions, Apache Airflow, dbt Cloud, and related tools.
• Implements data quality, reconciliation, monitoring, lineage, and auditing controls to ensure trusted, reliable, and compliant data assets.
• Ensures solutions adhere to enterprise data governance, information security, risk management, and regulatory requirements, including appropriate data access controls and encryption standards.
• Monitors, support and maintain cloud data platforms, pipelines, and infrastructure to ensure operational stability, reliability, and cost efficiency.
• Participates in production support activities, including incident management, root cause analysis, problem resolution, and continuous service improvement initiatives.
• Proactively monitors platform and pipeline performance, identifying and resolving issues before they affect business operations.
• Supports CI/CD processes through source control, automated testing, and deployment methodologies to enable efficient and reliable solution delivery.
• Leverages Infrastructure-as-Code (IaC) and automation practices to improve platform consistency, scalability, reliability, and operational efficiency.
• Collaborate with cross-functional teams to establish cloud architecture standards, best practices, and continuous improvement initiatives.
• Provides technical leadership, mentorship, and support to junior team members and business partners.
• Troubleshoots complex technical issues across cloud infrastructure, data platforms, and integration services.
• Stays current with emerging cloud, data engineering, analytics, and AI technologies to drive innovation and operational excellence.
• Adheres to and complies with applicable, federal and state laws, regulations and guidance, including those related to anti-money laundering (i.e. Bank Secrecy Act, US PATRIOT Act, etc.).
• Adheres to Bank policies and procedures and completes required training.
• Identifies and reports suspicious activity.

• Bachelor's Degree in Computer Science, Information Technology, Information Systems, Engineering, or a related field.
• Equivalent combination of education and relevant experience may be considered.

• Minimum of 3 years of experience in Data Engineering, Cloud Engineering, or a related technology role, required.
• Hands-on experience designing and supporting cloud-based data solutions utilizing AWS services, required.
• Experience with Snowflake Data Cloud, including data modeling, performance optimization, and security best practices, required.
• Experience developing and maintaining data transformation frameworks using dbt Cloud and/or dbt Core, required.
• Strong proficiency in SQL, Python, and PySpark for data transformation, automation, and analytics workloads, required.
• Experience building and supporting ETL/ELT pipelines in cloud environments, required.

• Experience with CI/CD practices and tools such as Git, GitHub, Jenkins, GitHub Actions, Terraform, or similar technologies.
• Experience with workflow orchestration tools such as Apache Airflow, AWS Step Functions, or similar platforms.
• AWS, Snowflake, or dbt certifications.
• Experience in financial services, banking, or other highly regulated industries.
• Exposure to data analytics, machine learning, or AI-enabled data platforms.

• Strong understanding of cloud architecture, including infrastructure as code (IaC) and containerization technologies.
• Familiarity with serverless architectures for data processing
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.
• Knowledge of data warehousing and business intelligence best practices.
• Familiarity with big data technologies such as Hadoop, Spark, and Kafka.