We are looking for an experienced Data Engineering Manager to lead the design, implementation, and optimization of our data systems and processes. Reporting to the Senior Manager of Data Engineering, this role is responsible for managing a team of data engineers, driving strategic data initiatives, and ensuring the delivery of high-quality, scalable solutions that support business needs. The ideal candidate will bring expertise in modern data platforms like Snowflake, Fivetran, and Airflow along with a strong focus on operational efficiency, team leadership, and technical excellence.
Qualification(s):
Education(s):
Bachelor of Science (B.S): Computer and Information Science
Work Experience:
Experience Range IV: 8 – 13 years of relevant experience or industry exposure in a related field
Skill(s):
Data Analytics, Data Architecture, Data Engineering, Data Integration, ETL (Informatica), ETL Tools, Snowflake (Platform), Structured Query Language (SQL)
Job Description:
Responsibilities:
Leadership
Foster a culture of ownership, collaboration, and continuous improvement within the team.
Data Architecture and System Design
Own the architecture and design of scalable, efficient data pipelines and systems to support business analytics, reporting, and operational needs.
Lead the adoption and optimization of Snowflake as the core data warehouse platform.
Monitoring and Operational Excellence
Establish robust monitoring processes for data pipelines, batch jobs, and workflows, including Snowflake and Airflow.
Address and resolve data quality issues such as schema mismatches, inconsistent data types, and field-level validations.
Security and Compliance
Enhance data security by implementing Snowflake multi-factor authentication (MFA) and adhering to best practices for access control.
Ensure compliance with data privacy regulations (e.g., GDPR, CCPA) and internal governance policies.
System Enhancements and Modernization
Lead efforts to phase out legacy systems (e.g., SAP BW, SAC) and migrate workflows to modern platforms.
Implement Change Data Capture (CDC) processes for real-time and incremental data ingestion.
Collaboration and Stakeholder Management
Work closely with analytics, business intelligence, and product teams to understand data requirements and deliver solutions that meet business goals.
Partner with IT and DevOps teams to ensure infrastructure scalability, performance, and reliability.
Innovation and Continuous Improvement
Evaluate and adopt emerging technologies and best practices to improve data workflows and system performance.
Promote automation and optimization of repetitive tasks to reduce manual intervention.
Drive initiatives for advanced analytics, real-time processing, and machine learning readiness.
Communicate complex technical concepts to non-technical stakeholders in a clear and effective manner.
Integrate metadata management tools like DataHub to improve data discoverability and governance.
Automate error detection and notification systems, integrating tools like Webex for real-time alerts.
Design and enforce data modeling standards, schema management, and table naming conventions.
Develop and implement data integration workflows using Fivetran and other modern ETL tools.
