Lead Data Engineer

Incedo•Austin, TX

1d•Remote

About The Position

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.

Requirements

Expertise in Azure services and tools such as Azure Data Factory, Azure Databricks, and Azure Synapse Analytics
Experience in building scalable and reliable data pipelines using Azure services, Apache Spark, and related big data technologies
Familiarity with cloud-based infrastructure and deployment, specifically on Azure
Strong knowledge of programming languages such as Python, C#, and SQL
Excellent communication skills and ability to communicate complex technical information to non-technical stakeholders in a clear and concise manner.
Understand the company's long-term vision and align with it.
Open to new ideas and willing to learn and develop new skills.
Ability to work well under pressure and manage multiple tasks and priorities.
Financial Data Fluency — understanding of wealth management data, positions, transactions, and reconciliation breaks.
Engineering Rigor — writing clean, testable, well-documented code and caring about reliability.
AI-Forward Mindset — actively incorporating AI tools into workflow.
Cross-Functional Collaboration — ability to work effectively with operations, service, and product teams.
Problem Ownership — seeing issues through to resolution and building guardrails to prevent recurrence.

Nice To Haves

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

Responsibilities

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
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
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
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