Lead the design and delivery of analytics-ready data models and transformation layers using dbt as the standard framework
Establish and enforce dbt development standards, including model design, documentation, testing, CI/CD, and release practices
Own delivery and operations of scalable ingestion, transformation, and delivery pipelines on AWS, ensuring reliability, performance, and cost efficiency
Partner with Cloud Engineering and Security to ensure AWS data solutions meet security, privacy, and compliance requirements
Implement monitoring, alerting, incident response practices, and runbooks for dbt and AWS workloads to improve operational stability
Drive strong data quality practices including source definitions, freshness checks, automated tests, and data lineage expectations
Collaborate with business stakeholders to translate needs into prioritized roadmaps and delivered data products
Manage and mentor data engineers and analytics engineers through coaching, performance management, and career development
Promote disciplined engineering practices across the team including code review standards, documentation, automation, and reusable frameworks
Enable future machine learning use cases by ensuring curated datasets are ML-ready, including feature readiness and foundational requirements for model operationalization
Evaluate and introduce platform improvements that strengthen scalability, maintainability, governance, and developer productivity

Requirements

Bachelor’s degree required, preferably in Computer Science, Engineering, or related technical field; Master’s degree preferred
AWS certifications (Data, Machine Learning, or Solution Architecture) are a plus
8 to 12 years of experience building enterprise-grade data platforms and pipelines
3 to 5+ years leading data engineering and/or analytics engineering teams in cloud-native environments
Demonstrated hands-on experience using dbt as a primary transformation framework in production, including testing, documentation, CI/CD, and release practices
Strong experience delivering data platforms on AWS (S3, Redshift, Glue, EMR, Lambda, Kinesis, SageMaker as applicable)
Experience supporting ML initiatives through strong data foundations, feature readiness, and platform enablement is preferred
Deep understanding of modern data modeling and analytics engineering concepts, including dbt best practices
Strong AWS data engineering expertise including scalability, reliability, and cost optimization
Strong leadership and people-management skills with a focus on coaching and developing talent
Ability to drive technical excellence while balancing speed, quality, and operational stability
Excellent problem-solving, analytical thinking, and decision-making skills
Strong communication and influencing skills across technical and business stakeholders
Comfortable working in a fast-paced, matrixed, and global environment

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

dbtdata modelingdata transformationCI/CDdata quality practicesmachine learning readinessdata pipelinesAWSfeature readinessanalytics engineering

Soft Skills

leadershipcoachingproblem-solvinganalytical thinkingdecision-makingcommunicationinfluencingperformance managementcollaborationoperational stability

Certifications

Bachelor's degreeMaster's degreeAWS Data certificationAWS Machine Learning certificationAWS Solution Architecture certification