Senior Data & Analytics Specialist

The Senior Data & Analytics Specialist is responsible for designing, building, and advancing the firm's enterprise data and analytics capabilities. This role combines expertise in data engineering, business intelligence, advanced analytics, and machine learning to deliver scalable data platforms, actionable business insights, and AI-ready data assets. Working closely with business and technology leaders, the position transforms enterprise data into trusted information that improves decision-making, operational efficiency, and client outcomes.

Responsibilities

Design, develop, and maintain the firm's enterprise data platform, including data warehouses, data lakes, semantic models, and data pipelines; Build scalable ETL/ELT processes that integrate information across Finance, Tax, Audit, Advisory, Operations, and other business systems while ensuring data quality, reliability, governance, and performance; Define data models, standards, and architecture that support reporting, analytics, machine learning, and AI initiatives; Develop modern analytics solutions that provide meaningful insights into business performance and operations; Design and deliver executive dashboards, KPIs, operational reporting, and self-service analytics using Power BI and Microsoft Fabric; Partner with business stakeholders to translate analytical requirements into scalable reporting solutions while establishing best practices for data visualization, metric definitions, and analytics governance; Apply statistical analysis, predictive modeling, forecasting, and machine learning techniques to solve complex business problems; Build analytical models that improve operational efficiency, identify trends, predict outcomes, and support strategic decision-making; Evaluate model performance, improve accuracy, and operationalize analytical solutions for enterprise use; Develop governed, high-quality data assets that enable AI applications, intelligent automation, and generative AI solutions; Support modern AI capabilities through semantic models, vector-ready datasets, retrieval pipelines, and data preparation processes; Partner with software engineering teams to integrate analytics and machine learning capabilities into enterprise applications and AI agents; Provide technical leadership in enterprise data architecture, analytics technologies, and modern data engineering practices; Evaluate emerging tools and technologies, recommend improvements to the firm's data ecosystem, and contribute to the long-term analytics and AI strategy; Promote engineering best practices, data governance standards, automation, and continuous improvement across the analytics platform

Requirements

5+ years of progressive experience in data engineering, data analytics, data science, business intelligence, or related technical disciplines; Degree in Computer Science, Data Science, Analytics, Engineering, or a related technical discipline; Strong experience designing relational databases, dimensional models, data warehouses, and scalable data pipelines; Advanced proficiency with SQL and Python, including data transformation, automation, and analytical development; Experience designing and implementing ETL/ELT processes, data integration solutions, and enterprise data models; Working knowledge of statistical analysis, predictive modeling, machine learning, and model evaluation techniques; Knowledge of DevOps, CI/CD, Git, and Infrastructure-as-Code practices for analytics platforms; Experience with data governance, data quality, metadata management, and enterprise analytics best practices; Strong analytical thinking, technical problem-solving, and the ability to translate business requirements into scalable data solutions; Excellent communication skills with the ability to explain complex technical concepts to business stakeholders

Nice to have

Experience within professional services, consulting, financial services, or public accounting; Hands-on expertise with Microsoft Fabric, Azure Data Platform, Power BI, or comparable cloud-based analytics platforms; Experience with AI-enabled data architectures, RAG pipelines, semantic search, vector databases, or LLM-powered applications; Experience building production machine learning or advanced analytics solutions; Master’s Degree in Computer Science, Data Science, Analytics, Engineering, or a related technical discipline

Benefits

variety of benefits and perks; hybrid work model