Data Analytics/Engineer (Python and MS Fabric exp)

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.
JOB QUALIFICATIONS
Education : Bachelor’s degree in data science/analytics or Engineering in Computer Science, or related quantitative field. Master’s degree preferred.
Experience :
At least 5+ years of experience in the field of Data Analytics/Engineering.
Proven track record of leading data engineering initiatives or mentoring junior data engineers.
Technical Skills :
Architecting scalable data solutions across cloud platforms.
Advanced performance tuning and optimization of data pipelines.
Experience with enterprise-level data governance frameworks.
Leadership in CI/CD and DevOps practices for data engineering.
Mentorship experience or team leadership in agile environments.
Experience with data pipeline tools (e.g., Apache Airflow, dbt, Azure Data Factory, Fabric, Informatica).
Proficient in SQL and Python/PySpark for complex data transformations and automation
Hands-on experience with cloud platforms (Fabric or AWS) and data warehouses (Snowflake or Synapse) for large-scale data integration and analysis
Familiarity with Microsoft Fabric and its integration within modern data ecosystems
Strong understanding of data modelling, schema design, and performance tuning
Knowledge of CI/CD pipelines and version control systems like Git
Soft Skills :
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.
ESSENTIAL DUTIES
Model Development & Deployment:
Develop predictive models to forecast key sales and marketing metrics solve to complex business problems and drive strategic insights.
Lead the end-to-end lifecycle of data science projects, including data preparation, feature engineering, model development, validation, and deployment.
Lead and architect predictive modeling frameworks.
Review and guide model development by junior team members.
Design and implement scalable analytics platforms.
Drive strategic data initiatives in collaboration with finance and business leaders.
Data Analysis & Insights:
Analyze large, complex datasets from multiple sources to identify trends, patterns, and opportunities that inform decision-making.
Collaborate with finance and accounting teams to automate reconciliations, variance analysis, and error detection using advanced analytics and machine learning.
Innovation, Automation & Generative AI:
Lead initiatives leveraging Generative AI (GenAI) technologies to automate the generation of financial reports, narratives, and data summaries, enhancing efficiency and accuracy.
Lead GenAI strategy for data engineering and analytics automation.
Evaluate and implement emerging technologies to enhance data workflows.
Explore and implement GenAI applications to improve natural language processing (NLP) capabilities for extracting insights from unstructured financial documents (e.g., contracts, invoices).
Drive the adoption of GenAI-powered chatbots or virtual assistants to support finance shared services teams with data queries, process guidance, and routine task automation.
OTHER DUTIES
Support Data Infrastructure & Governance:
Lead efforts to design, optimize, and scale data infrastructure in Microsoft Fabric, defining clear data requirements and overseeing robust ETL/ELT processes.
Drive data governance initiatives by implementing data quality frameworks, ensuring thorough documentation, and standardizing practices across teams to maintain data integrity.
Enhance Reporting & Analytics Standards:
Collaborate with analytics and BI teams to establish dashboard design standards, improve user experience, and ensure consistent KPI definitions across the organization.
Promote a Data-Driven Culture:
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.
Cross-Functional Collaboration:
Partner with business, product, and operational teams to provide expert data engineering support, enabling effective data-driven solutions and innovation across departments.
Our Interview Practices
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.