Senior Data Engineer

Incedo Inc. is a high-growth Digital, Data and AI Transformation Specialist firm headquartered in New Jersey. Weare a long-term strategy execution partner for Fortune 500 enterprises, operating at the intersection of businessand technology across Banking & Payments, Wealth Management, Telecom, Hi-Tech, and Life Sciences.We are building Incedo 4.0 - an AI-native, execution-focused, founder-led organization designed for scale, speed,and long-term impact.Incedo delivers ROI from AI @ Scale through the “Power of 3”:• Deep domain expertise• AI & Data capabilities• Engineering & Operations excellenceData Engineer — Wealth Management PlatformWe 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 ResponsibilitiesData Pipeline Development & EngineeringDesign, build, and maintain scalable data pipelines using Databricks and Azure cloud servicesDevelop and optimize PySpark and Python-based ETL/ELT workflows for ingesting, transforming, and serving wealth management dataBuild and manage data models that support advisor, account, client, position, transaction, and security datasetsEnsure data pipelines meet performance, reliability, and latency requirements for downstream consumersFinancial Data & ReconciliationReconcile financial datasets across custodians, internal systems, and third-party data providers — identifying and resolving breaks at the position, transaction, and account levelPartner with operations and service teams to investigate and resolve data discrepancies impacting advisors and clientsImplement data quality checks, validation rules, and alerting to proactively catch data integrity issuesSupport the build-out of reconciliation frameworks that scale across growing data volumes and entity countsCloud Infrastructure & PlatformBuild and manage data infrastructure on Microsoft Azure, including Azure Data Factory, Azure Data Lake, and related servicesContribute 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 codeAI-Augmented EngineeringActively leverage AI coding assistants and automation tools (e.g., GitHub Copilot, Claude, ChatGPT) to accelerate development, code review, and documentationIdentify opportunities to apply AI/ML techniques to financial data problems such as anomaly detection, break prediction, or data classificationStay current on emerging AI tooling and bring practical recommendations to the teamRequired Qualifications5–8 years of experience in data engineering, with direct exposure to wealth management data domainsDatabricks Certified (Associate or Professional) or demonstrated deep, hands-on Databricks expertise in a production environmentProficiency in Python and PySpark for building and optimizing large-scale data pipelinesHands-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 dataExperience reconciling financial datasets across custodians, platforms, or internal systemsStrong understanding of data modeling, ETL/ELT patterns, and data warehouse or lakehouse architectureDemonstrated 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 smarterPreferred QualificationsExperience with Delta Lake, Unity Catalog, or Databricks Asset BundlesFamiliarity with custodial data feeds and formats (Schwab, Fidelity, Pershing, or similar)Exposure to advisor technology platforms such as Addepar, Black Diamond, Envestnet, Orion, or TamaracExperience with dbt (data build tool) for transformation layer developmentKnowledge of financial instruments including equities, fixed income, alternatives, and managed accountsFamiliarity with data governance, data lineage, and metadata management practicesExperience in a fintech, WealthTech, RIA, or asset management environment