Sr. Data Engineer

Anblicks; Dallas (TX); On-site; USD 110,000 - 150,000; Full time

Job summary

Anblicks in Dallas is seeking a data engineer to design, build, and maintain scalable data pipelines that support analytics, reporting, and BI needs.

You will optimize dbt workflows, architect Snowflake data models, integrate data from multiple sources using ETL/ELT tools, troubleshoot issues, and document architecture for the team. The role emphasizes collaboration with data analysts and business stakeholders to drive reliable insights.

Qualifications

• Experience designing and implementing scalable data pipelines.
• Strong SQL and data modeling skills.
• Ability to collaborate with analysts and stakeholders.
• Familiarity with data quality checks and monitoring.

Responsibilities

• Design, build, and maintain scalable data pipelines for analytics and BI.
• Develop and optimize dbt workflows with modularity and tests.
• Architect Snowflake data models and warehousing for performance.
• Integrate data from multiple sources using ETL/ELT tools.
• Troubleshoot pipeline issues with strong analytical skills.
• Document architecture and pipeline logic for knowledge sharing.
• Implement data quality checks, monitoring, and alerts.
• Optimize SQL queries and warehouse performance.
• Stay current with new data engineering tools and practices.
• Collaborate with analysts, scientists, and stakeholders.

Skills

SQL

Python

Data Modeling

Analytics

Collaboration

Tools

dbt

Snowflake

ETL Tools

Airflow

Job description

• Design, build, and maintain scalable, reliable data pipelines that support analytics, reporting, and business intelligence needs
• Develop and optimize data transformation workflows using dbt, following best practices for modularity, testing, and documentation
• Architect and manage data models and warehousing solutions within Snowflake, ensuring performance, scalability, and cost efficiency
• Integrate data from multiple source systems using ETL/ELT tools, ensuring data quality, consistency, and timeliness
• Troubleshoot and resolve complex data pipeline issues, applying strong analytical and problem-solving skills
• Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and translate them into robust technical solutions
• Implement data quality checks, monitoring, and alerting to ensure pipeline reliability
• Optimize SQL queries and warehouse performance (clustering, partitioning, query tuning)
• Document data architecture, pipeline logic, and processes for team knowledge sharing
• Stay current with emerging data engineering tools and practices, recommending improvements where relevant