• Design, develop, and maintain scalable and efficient data pipelines to process data from various sources using tools such as Matillion, HVR and Databricks • Collaborate with data scientists, analysts, architects and other stakeholders to understand and document data requirements and translate them into technical solutions • Develop and optimize ETL (Extract, Transform, Load) processes to ensure data quality and integrity • Utilize Snowflake for data warehousing, ensuring efficient data storage and retrieval • Perform data modeling to design and structure databases that support business requirements • Write and optimize SQL queries for data transformation • Ensure data security and compliance with industry standards and regulations • Adhere to and advocate established best practices, design patterns, and data governance mandates • Document data processes, workflows, and technical specifications • Conduct data analysis to support business insights and decision-making

• Own the end-to-end design, delivery, and operation of major data products, including high-throughput ETL/ELT pipelines, transformation workflows, and storage solutions. • Partner with engineering, product, and business stakeholders to clarify requirements, challenge assumptions, define pragmatic solutions, and drive projects from design through production launch. • Solve difficult data engineering problems by developing performance-tuned transformations and services using Python, high-performance SQL, and tools such as dbt. • Make sound technical trade-offs at the product level, balancing scalability, reliability, delivery speed, maintainability, and cost. • Author and drive technical designs and RFCs that clearly communicate architecture, alternatives, risks, and operational considerations. • Influence the technical direction of the data products you own and contribute to the team’s broader technical strategy and roadmap. • Set a high quality bar through thoughtful design and code reviews, automated testing, version-controlled schemas, and CI/CD practices for data pipelines. • Lead projects that improve the team’s engineering and operational excellence, including data observability, lineage, incident response, deployment safety, and developer productivity. • Design and operate scalable batch and real-time data solutions using technologies such as Spark, Flink, and Kafka. • Partner with the ML Platform team to deliver clean, reliable, and feature-rich datasets for model training and inference. • Advance data stewardship through strong documentation, discoverability, privacy controls, access management, and governance practices. • Lead postmortems for data incidents, identify systemic improvements, and drive corrective actions to completion. • Break complex projects into clear, deliverable work and help coordinate execution across engineers on the team. • Mentor other engineers, provide actionable technical feedback, and participate in interviewing and hiring. • Participate in the team's on-call rotation, responding to incidents, troubleshooting issues, and contributing to root cause analysis and service reliability improvements. • Respond to and troubleshoot production issues, outages, and critical alerts during assigned on-call shifts. • Escalate incidents as needed and collaborate with team members to restore service. • Document issues and contribute to improvements that reduce recurring incidents. • Be available to provide occasional after-hours, weekend, and holiday support while on call.

• Design, build, and operate large-scale data processing pipelines handling multi-terabyte and streaming datasets, including audio/video transcoding, feature extraction, and preprocessing workflows. • Deploy, scale, and troubleshoot containerized workloads on Kubernetes and AWS in production environments. • Build and maintain distributed data processing jobs using frameworks such as Spark and Ray. • Design and operate workflow orchestration systems (e.g., Airflow) with dependency management, retries, monitoring, and alerting for production pipelines. • Administer and tune enterprise databases, including performance tuning, backup/recovery, access control, and scaling strategies. • Partner with ML engineers and researchers to support training pipelines, model retraining triggers, feature stores, and other MLOps workflows.

Role Description 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. - Build and optimize scalable data pipelines, integrations, and data products utilizing AWS Services, Snowflake, dbt, Python, SQL, and related technologies. - Collaborate with a talented team of engineers, product partners, and business stakeholders to deliver cloud-based data solutions that support critical business initiatives. - Work with modern technologies, solve complex data challenges, and contribute to enterprise-scale data platforms that enable data-driven decision-making across the bank. Qualifications - 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. - 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. Requirements - 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. Benefits Benefits are an integral part of total rewards and First Citizens Bank is committed to providing a competitive, thoughtfully designed and quality benefits program to meet the needs of our associates. More information can be found at https://jobs.firstcitizens.com/benefits .