Senior Data Engineer

Incedo Inc. is a high-growth Digital, Data and AI Transformation Specialist firm headquartered in New Jersey. We are a long-term strategy execution partner for Fortune 500 enterprises, operating at the intersection of business and 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”: • Engineering & Operations excellence Key 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 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 Required Qualifications 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 #J-18808-Ljbffr Incedo Inc.