The Role
Leads end-to-end data engineering solutions, including scalable Snowflake and dbt pipelines, data modeling, SQL transformations, CDC, ELT, quality frameworks, monitoring, and production deployment. Designs engineering standards, troubleshoots complex data issues, improves performance and cost efficiency, collaborates with stakeholders, leads technical reviews, and mentors engineers.
Summary Generated by Built In
- Lead the design, development, optimization, and maintenance of scalable data pipelines using Snowflake and dbt.
- Own data engineering solutions end-to-end, from requirements analysis and data modeling through development, testing, and production deployment.
- Design efficient data models and transformation frameworks supporting enterprise analytics and business consumption.
- Develop and optimize complex SQL transformations, incremental processing, CDC, and ELT workflows.
- Troubleshoot complex data issues, perform root cause analysis, and implement sustainable engineering solutions.
- Establish engineering best practices around code quality, modularity, reusability, performance optimization, and cost efficiency.
- Build robust data quality, reconciliation, monitoring, and validation frameworks.
- Collaborate with architects, business stakeholders, and engineering teams to translate business requirements into scalable technical solutions.
- Lead technical design reviews, mentor engineers, and ensure high-quality deliverables.
- Identify opportunities for automation, simplification, and continuous improvement.
- Snowflake: Strong hands-on expertise in Snowflake architecture, performance tuning, query optimization, warehouse sizing, Snowpark, and data loading strategies.
- dbt: Advanced experience with dbt models, incremental strategies, macros, Jinja, testing, snapshots, documentation, and CI/CD integration.
- SQL: Expert-level SQL skills, including complex joins, window functions, query optimization, and troubleshooting.
- Python: Strong programming skills for data engineering, automation, and integration.
- Data Modeling: Experience with dimensional modeling, medallion architecture, data warehousing, and enterprise data models.
- Data Engineering: Strong understanding of ETL/ELT, batch and incremental processing, CDC, data quality, and reconciliation.
- Cloud Platforms: Experience with Azure data services.
- DevOps: Experience with Git, CI/CD pipelines, deployment automation, and engineering standards.
Skills Required
- Strong hands-on expertise with Snowflake architecture, performance tuning, query optimization, warehouse sizing, Snowpark, and data loading strategies
- Advanced experience with dbt models, incremental strategies, macros, Jinja, testing, snapshots, documentation, and CI/CD integration
- Expert-level SQL skills, including complex joins, window functions, query optimization, and troubleshooting
- Strong Python programming skills for data engineering, automation, and integration
- Experience with dimensional modeling, medallion architecture, data warehousing, and enterprise data models
- Strong understanding of ETL/ELT, batch and incremental processing, CDC, data quality, and reconciliation
- Experience with Azure data services
- Experience with Git, CI/CD pipelines, deployment automation, and engineering standards
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The Company
What We Do
Anblicks is a Cloud Data Analytics Company based out of Dallas, TX, with offices in USA, India, and Australia. Since 2004, Anblicks has been helping customers by bringing value to their data and implementing modern data architecture and advanced analytics solutions in the cloud.
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