Data Analytics/Engineer (Python and MS Fabric Exp)

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 a 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:

• Develop predictive models to forecast key sales and marketing metrics, solve 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.
• 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.
• 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:

• 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.
• Collaborate with analytics and BI teams to establish dashboard design standards, improve user experience, and ensure consistent KPI definitions across the organization.
• 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.
• Partner with business, product, and operational teams to provide expert data engineering support, enabling effective data-driven solutions and innovation across departments.