Data Analyst - Python, SQL & PowerBI

Responsibilities:

Partner with business stakeholders to assess objectives, capture requirements, and translate them into analytics and reporting solutions.
Design, develop, and maintain dashboards, reports, and self-service datasets using tools such as Power BI, Tableau, or similar platforms.
Define and operationalize KPI definitions, calculations, and business rules; document metrics and data lineage for transparency and reuse.
Perform exploratory analysis to identify trends, patterns, anomalies, and opportunities; communicate findings through clear storytelling and data visualization.
Provide end-user support and enablement (training, documentation, office hours) to improve adoption and data literacy.
Design, build, and maintain scalable ETL/ELT pipelines (batch and/or streaming) to ingest, transform, and curate data for analytics and operational use cases.
Develop and optimize data models (dimensional models, lakehouse/warehouse schemas) to enable performant reporting and consistent analytics.
Implement data quality checks, monitoring, and automated validation to ensure accuracy, completeness, and timeliness of datasets.
Troubleshoot and resolve data issues across source systems, pipelines, and semantic/reporting layers; perform root-cause analysis and implement preventive controls.

Required:

Bachelor’s degree in Computer Science, Information Systems, Engineering, Analytics, or a related field (or equivalent practical experience).
3+ years of experience across BI/reporting and data engineering (or demonstrated ability delivering end-to-end data-to-dashboard solutions).
Strong SQL skills, including complex joins, window functions, query optimization, and working with relational databases.
Experience building dashboards and semantic models in a BI platform (e.g., Power BI, Tableau, Qlik) and translating business requirements into reporting solutions.
Hands-on experience building data pipelines using Python and/or Spark/PySpark, including data transformation and orchestration concepts.
Working knowledge of data modeling (dimensional modeling, star/snowflake schemas) and BI best practices.
Strong communication skills with the ability to explain technical topics to non-technical stakeholders.