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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - Wealth Management Platform - **Company:** TUPPL Technology Inc - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Microsoft Azure, Microsoft Online Services, Cloud Computing, Code Review, Data Architecture, Data Validation, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Warehousing, DevOps, Python (Programming Language), Machine Learning, Meta-Data Management, Azure Data Lake, Data Classification, Azure Data Factory, GitHub Copilot, Data Build Tool (dbt), Data Lakes, Pyspark, Data Lineage, Enterprise Integration, Data Management, Azure Synapse Analytics, GPT, Data Pipelines, Databricks - **Published:** May 27, 2026 - **Apply:** https://www.dice.com/job-detail/93bb2a11-b485-4082-8035-bddbd8cc57b8 ## About the Role * 5-8 years of experience in data engineering, with direct exposure to wealth management data domains * Databricks Certified (Associate or Professional) or demonstrated deep, hands-on Databricks expertise in a production environment * Proficiency in Python and PySpark for building and optimizing large-scale data pipelines * Hands-on experience with Microsoft Azure cloud services (Azure Data Factory, Azure Data Lake Storage, Azure Synapse, or equivalent) * Direct experience working with wealth management data including positions, transactions, accounts, clients, advisors, and security master data * Experience reconciling financial datasets across custodians, platforms, or internal systems * Strong understanding of data modeling, ETL/ELT patterns, and data warehouse or lakehouse architecture * Demonstrated use of AI tools in day-to-day engineering work - this is not optional; we expect engineers to be actively leveraging AI to move faster and work smarter Preferred Qualifications * Experience with Delta Lake, Unity Catalog, or Databricks Asset Bundles * Familiarity with custodial data feeds and formats (Schwab, Fidelity, Pershing, or similar) * Exposure to advisor technology platforms such as Addepar, Black Diamond, Envestnet, Orion, or Tamarac * Experience with dbt (data build tool) for transformation layer development * Knowledge of financial instruments including equities, fixed income, alternatives, and managed accounts * Familiarity with data governance, data lineage, and metadata management practices * Experience in a fintech, WealthTech, RIA, or asset management environment Key Competencies * Financial Data Fluency - You speak the language of wealth management data and understand what positions, transactions, and reconciliation breaks mean to the business * Engineering Rigor - You write clean, testable, well-documented code and care about the reliability of what you build * AI-Forward Mindset - You actively incorporate AI tools into your workflow and treat them as force multipliers, not novelties * Cross-Functional Collaboration - You can work effectively with operations, service, and product teams to understand data needs and translate them into engineering solutions * Problem Ownership - You don''t just find issues in data; you see them through to resolution and build guardrails to prevent recurrence ## Description We are seeking a skilled Data Engineer with a strong wealth management background to join our data and technology team. This role sits at the intersection of financial data and modern cloud engineering - you will design, build, and maintain the data pipelines and infrastructure that power our advisor and client reporting, reconciliation processes, and platform integrations. The ideal candidate brings hands-on experience with Databricks and the Microsoft cloud ecosystem, a deep understanding of wealth management data domains, and the ability to leverage AI tooling to accelerate their daily work. Key Responsibilities Data Pipeline Development & Engineering * Design, build, and maintain scalable data pipelines using Databricks and Azure cloud services * Develop and optimize PySpark and Python-based ETL/ELT workflows for ingesting, transforming, and serving wealth management data * Build and manage data models that support advisor, account, client, position, transaction, and security datasets * Ensure data pipelines meet performance, reliability, and latency requirements for downstream consumers Financial Data & Reconciliation * Reconcile financial datasets across custodians, internal systems, and third-party data providers - identifying and resolving breaks at the position, transaction, and account level * Partner with operations and service teams to investigate and resolve data discrepancies impacting advisors and clients * Implement data quality checks, validation rules, and alerting to proactively catch data integrity issues * Support the build-out of reconciliation frameworks that scale across growing data volumes and entity counts Cloud Infrastructure & Platform * Build and manage data infrastructure on Microsoft Azure, including Azure Data Factory, Azure Data Lake, and related services * Contribute to the architecture and governance of the data lakehouse environment within Databricks (Delta Lake, Unity Catalog) * Collaborate with platform and DevOps teams on CI/CD pipelines, environment management, and data infrastructure as code AI-Augmented Engineering * Actively leverage AI coding assistants and automation tools (e.g., GitHub Copilot, Claude, ChatGPT) to accelerate development, code review, and documentation * Identify opportunities to apply AI/ML techniques to financial data problems such as anomaly detection, break prediction, or data classification * Stay current on emerging AI tooling and bring practical recommendations to the team ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)