Senior Data Engineer

First Citizens Bank•Raleigh, NC

20h•Remote

About The Position

This is a remote role that may only be hired in the following location(s): North Carolina, Arizona, Florida and Texas. We are seeking an experienced Senior Data Engineer to join our Data Platform Engineering Organization and help build scalable cloud-based data solutions that enable analytics and data-driven decision-making across the organization. The ideal candidate is a hands-on data engineer with expertise in cloud-based data platforms, data warehousing, ETL/ELT development, and modern data engineering practices. This individual will build and optimize scalable data pipelines, integrations, and data products utilizing AWS Services, Snowflake, dbt, Python, SQL, and related technologies. As part of our growing Cloud Data & AI Platform organization, you will collaborate with a talented team of engineers, product partners, and business stakeholders to deliver cloud-based data solutions that support critical business initiatives. This is an opportunity to work with modern technologies, solve complex data challenges, and contribute to enterprise-scale data platforms that enable data-driven decision-making across the bank.

Requirements

Bachelor's Degree and 4 years of experience in Data Engineering, Data Warehousing, Cloud Data Platforms, or related technology disciplines.
OR High School Diploma or GED and 8 years of experience in Data Engineering, Data Warehousing, Cloud Data Platforms, or related technology disciplines.

Nice To Haves

Advanced experience with cloud platforms such as AWS, Azure, or GCP.
Advanced experience with modern data warehousing and large-scale data processing technologies such as Snowflake, Spark, or equivalent platforms.
Advanced proficiency in Python and SQL.
Advanced experience developing and supporting ETL/ELT processes, data pipelines, and data integration solutions utilizing tools such as dbt, Apache Airflow, Astronomer or equivalent cloud-native data engineering technologies.
Experience with data modeling, data warehousing, and data engineering best practices.
Experience designing, developing, and supporting scalable and reliable data engineering solutions in enterprise environments.
Experience collaborating with business and technology stakeholders to deliver data-driven solutions.
Strong analytical, problem-solving, and communication skills.
Intermediate experience with enterprise ETL/ELT tools such as DataStage, SAP BODS, SSIS, Informatica, or equivalent technologies.
Intermediate experience developing and supporting Operational Data Stores (ODS), analytics platforms, and data products.
Intermediate experience with Change Data Capture (CDC), streaming, and real-time data ingestion technologies such as Kafka, Amazon Kinesis, Apache Flink, Qlik Replicate, Snow pipe Streaming, or equivalent platforms.
Intermediate experience integrating data across cloud and on-premises environments.
Experience with Data Vault modeling and modern data warehousing methodologies.
Intermediate understanding of data governance, security, compliance, and data quality management practices.
Experience working within Financial Services, Banking, or other highly regulated industries.
Familiarity with Tableau, Power BI, or other analytics and visualization platforms.
Experience using AI-powered development and productivity tools to improve engineering efficiency and delivery outcomes.
AWS Certified Data Engineer – Associate, AWS Certified Developer – Associate, SnowPro Core Certification, SnowPro Advanced Data Engineer Certification, or equivalent cloud and data platform certifications.

Responsibilities

Design, develop, and support scalable data pipelines, data warehouses, and cloud-based data solutions utilizing AWS Services, Snowflake, dbt, Python, SQL, and related technologies.
Develop and maintain ETL/ELT processes, data integrations, and transformation frameworks supporting operational, reporting, and analytical workloads.
Implement and enhance data quality controls, reconciliation processes, testing frameworks, and dbt tests to ensure trusted and reliable data assets.
Optimize data pipeline performance and operational efficiency through monitoring, alert investigation, automation, testing, CI/CD, dashboard development, and production support activities.
Develop monitoring dashboards and operational metrics to improve platform visibility, reliability, and incident response.
Support deployment automation, release validation, and environment promotion processes to improve delivery reliability and operational efficiency.
Collaborate with business and technology stakeholders to understand requirements and deliver scalable data solutions that support business objectives.
Contribute to engineering best practices through technical discussions, peer reviews, documentation, and continuous improvement initiatives across the data engineering team.
Leverage AI-powered development and productivity tools to improve engineering efficiency, code quality, testing, and delivery outcomes.