Senior Data Analytics Engineer (5+ years only with Python, Power BI, SQL)

Wolters Kluwer is a global leader in professional information services that combines deep domain knowledge with specialized technology. Our portfolio offers software tools coupled with content and services that customers need to make decisions with confidence. Every day, our customers make critical decisions to help save lives, improve the way we do business, build better judicial and regulatory systems. We help them get it right.

Minimal travel might be required.

• 5+ years of hands-on experience in data analysis, business intelligence, or a related analytics role.
• Proven track record of leading analytical initiatives, building and deploying AI/ML models, and delivering enterprise-grade dashboards to senior leadership.
• Demonstrated experience mentoring junior analysts and guiding cross-functional data projects.

• Strong proficiency in Python for data manipulation (Pandas, NumPy), statistical analysis (SciPy, Stats models), visualization (Matplotlib, Seaborn, Plotly), and end-to-end automation of analytical workflows. Experience with PySpark for large-scale data processing is a plus.
• Expert-level skills in Power BI (DAX, Power Query, data modeling) and/or Tableau. Proven ability to architect enterprise-wide dashboard frameworks, establish design standards, ensure consistent KPI definitions, and drive adoption across stakeholder groups.
• Extensive hands-on experience building, validating, and deploying predictive, classification, and clustering models using frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch. Strong command of feature engineering, hyperparameter tuning, model evaluation, and production deployment strategies.
• Experience with GenAI tools and frameworks (e.g., LangChain, OpenAI APIs, Azure OpenAI) for automating report generation, NLP-based insight extraction from unstructured data, or chatbot-driven analytics support.
• Advanced SQL skills for querying complex datasets across multiple sources. Strong understanding of data modeling, schema design, performance tuning, and data quality principles.
• Familiarity with Microsoft Fabric and its integration within modern data ecosystems, Azure Data Factory, or similar cloud-based platforms.
• Knowledge of Git, CI/CD pipelines, and DevOps practices for reproducible and scalable analytical workflows.
• Background in finance domain is a plus.

• Strong analytical skills; capable of multi-tasking in fast-paced, dynamic environment
• Strong written and verbal communication, including report writing and data storytelling
• Stay updated with industry trends and evolving tools; Demonstrate a proactive approach to learning new techniques and technologies
• Work effectively across cross-functional teams
• Strong stakeholder management and ability to translate business needs into technical solutions.
• Experience presenting technical concepts to non-technical audiences.
• Proven ability to lead cross-functional initiatives and drive consensus.

• Architect, build, and maintain enterprise-grade interactive dashboards in Power BI that provide real-time visibility into key business metrics across sales, marketing, and finance functions.
• Establish and enforce dashboard design standards, improve user experience, and ensure consistent KPI definitions across the organization.
• Automate recurring reporting workflows using Python scripts or Fabric Data flows integrated with BI tools to reduce manual effort, improve accuracy, and scale reporting capabilities.
• Design and implement scalable analytics platforms that serve as a single source of truth for organizational metrics.

• Analyze large, complex datasets from multiple sources to identify trends, patterns, and opportunities that inform strategic decision-making.
• Collaborate with finance and accounting teams to automate reconciliations, variance analysis, and error detection using advanced analytics and machine learning.
• Lead deep-dive analyses on key sales, marketing, and financial metrics, translating findings into actionable recommendations for senior leadership.
• Review and guide analytical work produced by junior team members, ensuring quality, accuracy, and alignment with business objectives

• Automate financial reporting and narrative generation.
• Extract insights from unstructured documents
• Build chatbots/assistants for finance teams
• Implement agentic AI/workflow automation

• Collaborate with data engineering teams to ensure data availability, quality, and accessibility for advanced analytics and modeling. 

• Participate in data governance initiatives by validating data integrity, documenting data sources, and standardizing datasets for consistent use across projects. 

• Contribute to the development of analytics frameworks, dashboards, and reporting tools that translate complex models into actionable business insights. 

• Work closely with BI teams to refine KPI definitions and improve visualization standards for clearer communication of analytical results.

• Advocate for a data-driven culture, promoting the value of analytics in strategic and operational decision-making.
• Lead or support analytical training sessions or workshops for non-technical stakeholders to improve data literacy across the organization.

• Collaborate on cross-functional initiatives, applying AI-driven solutions, machine learning, and data analytics to support business objectives. 

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.