Support strategic and operational decision-making through rigorous data analysis, automation, reporting, and visualization.
Requirements
- Expertise in SQL and Python, with hands-on experience using Pandas and PySpark for analytical workflows.
- Deep understanding of data modeling, ETL design, and performance optimization for analytical queries.
- Familiarity with EKS and EMR deployments, as well as data frameworks such as Apache Airflow, Flink, and Kafka, is a plus.
Responsibilities
- Design, build, and maintain automated data pipelines and dashboards to support business operations, planning, and performance tracking.
- Conduct data validation, cleansing, and transformation on structured and semi-structured datasets using SQL, Python, and Spark.
- Perform exploratory data analysis and develop ad-hoc analytical models to identify trends, anomalies, and optimization opportunities.
- Support data governance, quality assurance, and contract validation efforts within the enterprise data platform.
- Develop and maintain KPI frameworks across finance, supply chain, and operations domains; ensure data definitions are consistent across systems.
- Translate complex data into clear, actionable insights through well-structured narratives, visualizations, and business presentations.
- Contribute to the development of analytical feature stores and assist Data Science teams with dataset preparation for modeling and ML pipelines
Other
- Partner closely with engineering, finance, operations, and product teams to align analytical outputs with business priorities.
- Strong communication skills, with the ability to articulate analytical findings and recommendations to technical and non-technical stakeholders alike.
- Experience in business analytics, forecasting, or operational planning within enterprise environments preferred.
