Data Analytics Engineer (Operations)

The Analytics Engineer transforms raw, fragmented data into trusted, scalable, and analytics-ready datasets that power operational excellence and strategic decision-making. This role sits at the intersection of data engineering and analytics, combining SQL, Python, BI development, and business understanding. You will design data models, optimize pipelines, build dashboards, and partner closely with stakeholders to ensure data accuracy, insights quality, and alignment with business needs.

Key Responsibilities

Data Engineering & Modeling

• Build modular, maintainable, and performant ETL/ELT pipelines using SQL, Python, and engineering best practices.
• Design, maintain, and optimize data models (fact tables, dimensions, semantic layers, cubes).
• Implement data quality checks, validation, and monitoring frameworks to ensure reliable datasets.
• Improve query performance through indexing, partitioning, and efficient schema design.

Analytics & Insight Development

• Write advanced SQL and Python transformations, aggregations, and analytical logic.
• Apply foundational statistical methods (sampling, hypothesis testing, regression) when required.
• Identify trends, drivers, correlations, and conduct root-cause analyses across operational KPIs.
• Translate analytical findings into actionable recommendations for mid-level operational decisions.
• Experience with machine learning, predictive modeling, and automation frameworks is a plus.

BI & Visualization

• Build clear, impactful dashboards and self-service reports using Power BI, Tableau, or Looker
   (experience with SSAS and SSRS is an advantage).
• Define and maintain business metrics, KPI logic, and analytical documentation.
• Communicate insights effectively to both technical and non-technical stakeholders.

Collaboration & Delivery

• Partner with business teams to gather requirements, define scope, and align on deliverables.
• Document data flows, methods, dashboards, and analytical outputs.
• Ensure consistent communication on progress, timelines, and stakeholder satisfaction.
• Support cross-functional initiatives, benchmarking studies, and process improvement efforts.

Requirements

• Bachelor’s or Master’s degree in Data Science, Engineering, Statistics, Computer Science, or related field.
• 3+ years of experience in analytics engineering, business intelligence, or advanced analytics.
• Strong proficiency in SQL, Python, and Excel.
• Experience with BI tools such as Power BI, Tableau, or Looker (SSAS/SSRS exposure is a plus).
• Solid understanding of ETL/ELT workflows, data modeling principles, and query optimization.
• Ability to work independently, prioritize tasks, and solve moderately complex analytical problems.
• Excellent communication skills with the ability to tailor insights to diverse audiences.