Lead Data Engineer
Experience Range: 8- 10 years of experience in data engineering, with hands-on expertise in Snowflake, dbt, and AWS cloud services Key Responsibilities:
Develop, maintain, and optimize dbt models, macros, and tests to support scalable ETL/ELT data pipelinesAdminister and manage Snowflake data warehouses, including databases, schemas, roles, and security configurations to ensure robust data governanceOptimize complex SQL queries and warehouse performance, reducing processing times and improving storage utilizationManage and integrate AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatch to build and maintain reliable cloud data infrastructureImplement and maintain CI/CD pipelines for automated deployment and release management of data solutionsConfigure and monitor data quality checks, alerting systems, and performance monitoring to ensure high data reliability and integrityTroubleshoot and resolve production issues, conducting thorough root cause analysis to minimize downtime and prevent recurrenceCollaborate closely with analytics, business intelligence, and engineering teams to deliver high-impact, scalable data solutions aligned with business objectivesMedallion modeling: Design, build, and maintain dbt models across Bronze → Silver → Gold layers for the assigned domain.Governance alignment: Partner with the Data Governance team so models meet certification and quality-gate standards before promotion.Downstream support: Support data modeling for downstream analytics platform consumers (dashboards and data products)Troubleshooting & support: Diagnose and resolve pipeline issues; participate in on-call/support rotation as needed.Documentation: Document data lineage, model logic, and key technical decisions so the work is maintainable by the internal team.Maintain and Develop APIsRequired Skills:
Advanced proficiency in SQL (basic and advanced)Expertise in developing and managing dbt models, macros, and testsHands-on experience with Snowflake data warehousing, including Time Travel and Fail Safe featuresStrong understanding of ETL/ELT fundamentals and best practicesProficiency in Python for data engineering and automation tasksAdministration of Snowflake warehouses, databases, schemas, and role-based securityManagement of AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatchImplementation and maintenance of CI/CD pipelines for data deploymentsConfiguration of monitoring, alerting, and data quality checks in cloud data platformsPreferred Skills:
Experience with modern data platform fundamentals and architectureExpertise in optimizing large-scale data pipelines for performance and cost efficiencyFamiliarity with data governance and compliance best practices in cloud environmentsKnowledge of infrastructure-as-code tools for cloud resource managementExposure to advanced Snowflake features such as data sharing and secure data exchangeDesired Qualifications:
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related fieldRelevant industry certifications in AWS, Snowflake, or dbt are highly desirable
