PTMA Financial Solutions is looking to evolve its data platform and strategy by building and optimizing a cutting-edge Microsoft Fabric-based data platform, guiding data architecture decisions, and integrating advanced analytics to drive business value.
Requirements
- Excellent SQL skills for data querying and performance tuning, and strong programming ability in Python (or similar language) for developing data pipelines and automation scripts.
- Deep understanding of relational databases, data modeling, and building data integration workflows.
- Hands-on experience with Microsoft Azure data services and architecture. This should include familiarity with tools such as Azure Data Factory or Synapse Pipelines, Azure Data Lake Storage (ADLS Gen2), Azure Synapse Analytics or SQL pools, and related Azure infrastructure.
- Strong knowledge of data warehousing concepts, star/schema design, and lakehouse architecture.
- Ability to design data models and pipelines that efficiently handle large volumes of data and support analytics use cases.
- Solid understanding of data governance and security best practices.
- Comfortable implementing data access controls, encryption, masking of sensitive data, and user permission models.
Responsibilities
- Own and manage the end-to-end Microsoft Fabric architecture for PTMA. This includes designing and organizing the OneLake unified data lake (the single, logical data repository for all analytics data), configuring capacities, and overseeing Fabric’s components (data pipelines, Dataflows Gen2, Data Warehouse/Lakehouse, etc.) and their integration.
- Lead the design and implementation of enterprise data pipelines and ETL/ELT processes to ingest, transform, and load data from a variety of sources. You will integrate data from core business systems into the Fabric platform.
- Design secure APIs and data access layers to enable external Large Language Model (LLM) usage (outside of Microsoft’s built-in Copilot) with company data.
- Own the monitoring and optimization of the data platform’s performance. Plan for capacity and scalability of the Fabric environment, including compute and storage management, to accommodate growing data volumes and user concurrency
- Developing and maintaining data workflows. Build and maintain pipelines that aggregate and transform data from diverse systems into a centralized warehouse and lakehouse, ensuring data is consistent, clean, and ready for analysis.
- Establish and enforce data engineering best practices including coding standards, naming conventions for datasets and fields, data contract definitions between systems, and robust testing/validation of pipeline outputs.
- Deploy and manage Microsoft Purview (or similar data governance tools) to achieve enterprise-grade data cataloging, classification, and lineage.
Other
- Act as the primary point-of-contact for data initiatives across teams (Investments, Finance, Client Services, Operations).
- Engage with stakeholders to understand their data needs and pain points, translating them into technical solutions and phased implementation plans.
- Guide platform-related decisions (tools, vendors, integrations) and govern data project priorities
- Establish data governance policies and practices that ensure compliance with financial industry regulations (FINRA, SEC) and standards such as SOC 2.
- Manage a small data team of two individual contributors (the Analytics Engineer & BI Lead, and the Business Data Engineer & Reporting Lead).
