Data Engineer

As a Data Engineer, you will play a vital role in bridging the gap between data engineering and analytics to deliver actionable insights that drive strategic decisions. You will design, build, and optimize scalable data pipelines, data models, and dashboards that enable business teams to make data-driven decisions with confidenceYou’ll collaborate closely with cross-functional teams—including marketing, sales, and operations—to transform raw data into valuable business intelligence, particularly in the fast-paced beauty and FMCG industries where data-driven insights are key to consumer understanding and growth.Key ResponsibilitiesDesign, develop, and maintain data pipelines and ETL processes using Airflow, Python, and Google Cloud Platform (GCP) tools.Build and optimize data warehouses in BigQuery, ensuring data integrity, scalability, and performance.Develop and maintain Power BI dashboards to provide key business metrics, trend analysis, and performance insights.Collaborate with business stakeholders to understand analytical needs and translate them into efficient data models and solutions.Implement data modeling best practices (star/snowflake schemas) to support analytics and reporting.Apply query optimization and performance-tuning techniques to enhance system efficiency.Ensure high data quality, consistency, and governance across data systems.Work cross-functionally with marketing, sales, and finance teams to provide insights that support campaign performance, product launches, and market expansion.RequirementsStrong proficiency in SQL (BigQuery) and Python for data processing, transformation, and automation.Hands-on experience with Airflow, Docker, and Google Cloud Platform services (BigQuery, Data Fusion, GCS).Skilled in building dynamic and insightful Power BI dashboards and reports.Solid understanding of data modeling principles (star/snowflake schemas) and query optimization techniques.Strong analytical thinking, problem-solving skills, and attention to detail.Excellent communication and collaboration skills with both technical and business stakeholders.Preferred QualificationsBachelor’s degree in Computer Science, Data Engineering, Statistics, or a related field.3+ years of experience in data analytics engineering, data warehousing, or business intelligence roles.Experience working in the beauty, cosmetics, or FMCG industries, with an understanding of sales, marketing, and consumer analytics.Familiarity with version control tools (e.g., Git) and CI/CD pipelines.Experience with other BI tools (e.g., Looker, Tableau) is an advantage.