Sr. Data Engineer
Hybrid · Dallas, Texas, United States or Folsom, California, United States
Job Summary
Senior Data Engineer to join Allworth Financial's Analytics & Insights team. Focus on modernizing legacy SQL-based processes into scalable, maintainable cloud data pipelines, building and reviewing production‐quality pipelines, and maturing governance practices. Responsibilities include designing and optimizing data pipelines using PySpark, Delta Lake, SQL; improving observability, error handling, and reliability; reviewing notebooks, SQL scripts, data models, and orchestration workflows; guiding Azure Synapse, Spark, Delta Lake, and related cloud data services; identifying architectural gaps and technical debt; implementing data quality checks, master data patterns, and entity resolution; assisting with data cataloging, lineage, and governance; partnering with stakeholders to translate reporting and analytics needs into scalable data models for BI tools like ThoughtSpot and Power BI; mentoring team members and raising engineering maturity; advising on security, cost, performance, and long-term maintainability.
Required Qualifications
- 5+ years of professional experience in data engineering, analytics engineering, data architecture, or a closely related role
- Strong SQL skills, including stored procedures, joins, window functions, CTEs, merge logic, and performance tuning
- Hands-on experience with PySpark or distributed data processing frameworks
- Experience designing and maintaining ETL/ELT pipelines in a cloud environment
- Experience with data lake or lakehouse architecture, including Delta Lake or similar table formats
- Strong understanding of data modeling concepts, including dimensional modeling, staging layers, curated layers, and semantic/reporting models
- Experience with pipeline orchestration, scheduling, dependency management, and failure recovery
- Ability to troubleshoot data quality issues, pipeline failures, schema drift, performance bottlenecks, and source system inconsistencies
- Familiarity with modern data governance concepts, including cataloging, lineage, ownership, access control, data definitions, and data quality monitoring
- Ability to communicate clearly with both technical and non-technical stakeholders
- Experience reviewing existing systems and recommending practical, incremental improvements
- Experience with Azure Synapse Analytics, Azure Data Lake Storage, Microsoft Fabric, Azure Data Factory, or related Azure data services
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