Location: Raleigh, NC
Salary: $110,000.00 USD Annually - $115,000.00 USD Annually
Description: Please send resumes to You must be able to accept w2 employment without sponsorship now or in the future. Not open for c2c arrangements.
Lead Data EngineerTechnologies: Snowflake, dbt, Qlik
Level: Senior / Lead
About the RoleWe are seeking a Lead Data Engineer to design, build, and scale modern, cloud-based data platforms that support critical business use cases. In this role, you will provide technical leadership, drive data architecture decisions, and partner closely with business and engineering teams to deliver reliable, secure, and high-quality data solutions.
ResponsibilitiesData Architecture & Strategy- Lead the design and implementation of scalable, high-performance data architectures aligned with business priorities.
- Define and evolve enterprise data strategy, standards, and best practices.
- Evaluate and integrate new tools and technologies to enhance data capabilities.
Data Pipeline Development- Design and build complex ELT/ETL pipelines supporting both real-time and batch processing.
- Develop and optimize high-volume, high-velocity data ingestion and transformation frameworks.
- Implement medallion architecture patterns using Snowflake, dbt, and streaming technologies.
Data Governance, Security & Quality- Establish and enforce data governance practices, policies, and standards.
- Design and implement data quality frameworks, monitoring, and validation processes.
- Ensure compliance with data security, privacy, and regulatory requirements, including sensitive data protection.
Performance Optimization & Reliability- Analyze, troubleshoot, and optimize performance across large-scale data systems.
- Implement robust logging, monitoring, alerting, and observability solutions.
- Design and maintain disaster recovery and high-availability strategies.
Testing, Automation & Release Management- Enforce automated testing and data validation standards.
- Build CI/CD pipelines for data workflows using GitLab, GitHub, and automation tools.
- Review and approve code deployments across development, QA, production, and DR environments.
Leadership & Collaboration- Mentor and coach data engineers; provide technical direction and architectural guidance.
- Partner with business stakeholders to translate data requirements into scalable solutions.
- Promote data literacy and best practices across cross-functional teams.
Minimum Qualifications- Extensive experience designing and building automated, metadata-driven data pipelines using modern cloud data platforms.
- Strong expertise in Snowflake, dbt Cloud, and Qlik Replicate (including Snowpipe and streaming frameworks).
- Advanced experience with SQL and Python/PySpark for large-scale data processing and automation.
- Hands-on experience with AWS services such as S3, Lambda, SQS, SNS, Glue, and RDS.
- Experience integrating data from multiple sources including SQL Server, Oracle, DB2 (mainframe), files, and Snowflake.
- Strong knowledge of data governance, data quality, data security, and compliance frameworks.
- Proven experience with orchestration tools such as dbt Cloud and Apache Airflow (Astronomer).
- Experience designing schema evolution and schema drift detection strategies.
- Proficiency in implementing RBAC, data access controls, and Snowflake data masking/tokenization policies.
- Strong problem-solving skills and experience with performance tuning and optimization.
- Experience with structured and semi-structured data formats (CSV, fixed width, JSON, XML, Excel, VSAM).
- One or more relevant technical certifications.
Preferred Qualifications- Financial services or banking domain experience.
- Experience leading production support and release activities in regulated environments.
- Demonstrated ability to influence technology investments and architecture decisions across business units.
What You'll Bring- A balance of hands-on engineering skills and technical leadership.
- Strong communication skills and the ability to work across technical and non-technical teams.
- A passion for building reliable, secure, and scalable data systems.
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