Sr. Data Engineer

United States·Dallassenior

Data EngineerData

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Overview

Requirements Summary

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.

Technical Tools

azurepower-bisparksqldata-analysisdatabase-designetlperformance-optimization

Allworth Financial (www.allworthfinancial.com) is an independent investment financial advisory firm that specializes in retirement planning, investment advising, and 401(k) management with a direct approach to financial planning. Allworth Financial delivers long- and short-term investment planning solutions and advice to help clients achieve their goals and plan strategically for retirement.

Allworth Financial is a high growth, private equity backed, multi branch Registered Investment Advisor. Founded in Sacramento, California, in 1993 Allworth is primarily a fee-based, employee-centric fiduciary advisory firm, that prides itself on emphasizing client well-being and education. The business is a multi-billion dollar firm and is on track for continued growth through both acquisitions and strong organic growth. Allworth is considered a great place to work and was once again given the prestigious “Circle of Excellence” award by the National Business Research Institute for employee and client satisfaction in 2021, placing it among the best-loved brands in America. Allworth Financial was also recognized as a Barron's Top 40 RIA in 2024.

This is an opportunity to join a high growth business as the Data Engineer. The position will work with the Analytics & Insights team at Allworth Financial to optimize the company’s existing data infrastructure to support a robust data analytics and reporting structure.

We are looking for a Senior Data Engineer, or similarly experienced data modernization professional, to help guide the next stage of our data platform evolution. This role will support the modernization of legacy SQL-based processes into scalable, maintainable cloud data pipelines, while also helping us identify architectural gaps, improve engineering standards, and mature our data governance practices.

The ideal candidate has broad hands-on experience across data engineering, cloud data platforms, ETL/ELT design, data modeling, orchestration, performance tuning, data quality, and governance. We are looking for someone who can both build and advise: someone who can write production-quality pipelines, review existing architecture, mentor team members, and point out the risks, patterns, and opportunities we may not yet know to look for.

This person will play a key role in helping us move from legacy stored procedures and fragmented reporting processes toward a more reliable, transparent, and well-governed modern data architecture.

This is a full-time Exempt location. Ideal candidate will be able to work hybrid work schedule in our Addison office.

Responsibilities

~1 min read

→Design, build, and optimize data pipelines using PySpark, Delta Lake, SQL, and cloud-based data engineering tools.
→Improve data pipeline reliability, observability, logging, error handling, and restartability.
→Review existing notebooks, SQL scripts, data models, and orchestration workflows for maintainability and performance.
→Guide best practices for Azure Synapse, Spark, Delta Lake, data lake storage, and related cloud data services.
→Identify architectural gaps, technical debt, and modernization risks.
→Help design and implement data quality checks, reconciliation processes, and validation frameworks.
→Support development of canonical IDs, master data patterns, and entity resolution processes.
→Assist with data cataloging, lineage, metadata management, and governance practices.
→Partner with business stakeholders to understand reporting, analytics, and data product requirements.
→Help structure scalable data models for BI tools such as ThoughtSpot, Power BI, or similar platforms.
→Mentor data team members and help raise the overall engineering maturity of the team.
→Provide guidance on what we may be overlooking in areas such as security, performance, cost, governance, orchestration, testing, and long-term maintainability.

Requirements

~1 min read

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.