Lead Data Engineer

Job requirements

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 pipelines

Administer and manage Snowflake data warehouses, including databases, schemas, roles, and security configurations to ensure robust data governance

Optimize complex SQL queries and warehouse performance, reducing processing times and improving storage utilization

Manage and integrate AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatch to build and maintain reliable cloud data infrastructure

Implement and maintain CI/CD pipelines for automated deployment and release management of data solutions

Configure and monitor data quality checks, alerting systems, and performance monitoring to ensure high data reliability and integrity

Troubleshoot and resolve production issues, conducting thorough root cause analysis to minimize downtime and prevent recurrence

Collaborate closely with analytics, business intelligence, and engineering teams to deliver high-impact, scalable data solutions aligned with business objectives

Medallion 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 APIs

Required Skills:

Advanced proficiency in SQL (basic and advanced)

Expertise in developing and managing dbt models, macros, and tests

Hands-on experience with Snowflake data warehousing, including Time Travel and Fail Safe features

Strong understanding of ETL/ELT fundamentals and best practices

Proficiency in Python for data engineering and automation tasks

Administration of Snowflake warehouses, databases, schemas, and role-based security

Management of AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatch

Implementation and maintenance of CI/CD pipelines for data deployments

Configuration of monitoring, alerting, and data quality checks in cloud data platforms

Preferred Skills:

Experience with modern data platform fundamentals and architecture

Expertise in optimizing large-scale data pipelines for performance and cost efficiency

Familiarity with data governance and compliance best practices in cloud environments

Knowledge of infrastructure-as-code tools for cloud resource management

Exposure to advanced Snowflake features such as data sharing and secure data exchange

Desired Qualifications:

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field

Relevant industry certifications in AWS, Snowflake, or dbt are highly desirable