Lead Data Engineer

We are seeking an experienced Lead Data Engineer to join our Data Platform Engineering Organization and lead the design, development, and modernization of enterprise data platforms that power analytics, reporting, business intelligence, and operational decision-making across the organization.

The ideal candidate is a hands-on data engineering leader with expertise in cloud-based data platforms, data warehousing, ETL/ELT development, and modern data engineering practices. This individual will be responsible for delivering scalable data solutions utilizing AWS Services, Snowflake, dbt, Python, SQL, and related technologies while mentoring team members and driving engineering excellence.

As part of our growing Cloud Data and AI Platform organization, you will lead and collaborate with a talented team of engineers, product partners, and business stakeholders to deliver enterprise data products and cloud-based data solutions. This role offers the opportunity to mentor engineering talent, drive engineering best practices, and support the adoption of modern data engineering capabilities across the organization., The successful candidate will have experience working in large enterprise environments and partnering with cross-functional teams to deliver trusted data products that support strategic business objectives.

• Lead the design, development, and implementation of enterprise data solutions utilizing AWS Services, Snowflake, dbt, Python, SQL, and related cloud technologies.
• Develop and support data warehouses, Operational Data Stores (ODS), and ETL/ELT pipelines that enable analytics, reporting, and operational data capabilities.
• Design and implement batch and real-time data processing solutions using modern streaming and event-driven architectures.
• Partner with business stakeholders, analysts, and technology teams to translate business requirements into scalable data solutions.
• Define and promote data quality strategies, testing standards, governance practices, and compliance controls across the data platform lifecycle.
• Establish enterprise testing practices, including data validation frameworks, dbt testing standards, and quality controls to support trusted data products.
• Define monitoring, observability, and operational reliability standards, including dashboards, alerts, service-level objectives (SLOs), and service-level agreements (SLAs) for enterprise data platforms.
• Establish deployment strategies, environment promotion standards, and release governance practices that support secure and reliable delivery across enterprise data platforms.
• Contribute to technical discussions, peer reviews, and knowledge-sharing initiatives across the data engineering team.
• Leverage AI-powered development and productivity tools to accelerate solution delivery, improve code quality, and enhance engineering productivity

Bachelor’s Degree and 6 years of experience in Data Engineering, Data Warehousing, Cloud Data Platforms, or related technology disciplines., High School Diploma or GED and 10 years of experience in the same areas., * 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 designing, developing, orchestrating, and supporting ETL/ELT pipelines and data integration solutions using tools such as dbt, Apache Airflow, Astronomer, or equivalent cloud-native data engineering technologies.
• Advanced experience with data modeling, data warehousing, and data engineering best practices.
• Experience designing, developing, and supporting scalable, reliable, and high-performing 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.

Following qualifications are a plus

• Advanced experience with enterprise ETL/ELT tools such as DataStage, SAP BODS, SSIS, Informatica, or equivalent technologies.
• Experience developing and supporting Operational Data Stores (ODS), analytics platforms, and data products.
• Experience with Snowflake Cortex, Change Data Capture (CDC), streaming, and real-time data ingestion technologies including Kafka, Amazon Kinesis, Qlik Replicate, Snow pipe Streaming, or equivalent platforms.
• Experience integrating data across AWS, on-premises, and Snowflake environments, with exposure to enterprise security, governance, and compliance frameworks.
• Experience with Data Vault modeling and modern data warehousing methodologies.
• Experience leading technical initiatives, mentoring team members, and promoting engineering best practices.
• Experience 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 Solutions Architect - Associate or Professional, SnowPro Core Certification, SnowPro Advanced Data Engineer Certification, or equivalent cloud and data platform certifications.