The company is a leading e-gift card distribution agency, committed to delivering seamless and innovative solutions to our clients. We are looking for a hands-on Data / Analytics Engineer to design, build, and scale our data platform. This role will be responsible for building our data warehouse, developing ETL pipelines, creating Power BI dashboards, and leveraging AI-driven analytics to unlock business insights and forecasting capabilities. You will work closely with business, product, finance, and engineering teams to turn raw data into actionable intelligence.
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Job Requirements:
- Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
- Minimum of 6 years of hands-on experience in data engineering, ETL/ELT development, and data platform implementation.
- Strong experience with Power BI.
- Strong SQL skills.
- Experience building ETL pipelines.
- Experience with AWS Redshift (or similar data warehouse: Snowflake, BigQuery).
- Knowledge of data modeling best practices.
- Understanding of REST APIs and data integrations.
- Experience using AI/ML tools for forecasting or analytics.
- Familiarity with predictive modeling techniques.
- Knowledge of statistical analysis.
Nice to Have:
- Experience with AWS ecosystem (Aurora, RDS, Glue).
- Experience with Airflow or orchestration tools.
- Knowledge of dbt.
- Experience with financial or transactional datasets.
Additional Skills:
- Strong analytical thinking.
- Business-oriented mindset.
- Ability to translate data into strategic recommendations.
- Independent, proactive, and ownership-driven.
- Strong communication skills.
Job Responsibilities:
Business Intelligence & Reporting:
- Design and build interactive Power BI dashboards and reports.
- Translate business requirements into data models and visual insights.
- Maintain and optimize semantic models and data performance.
- Ensure data accuracy, consistency, and usability.
Data Engineering & ETL:
- Design, develop, and maintain scalable ETL/ELT pipelines.
- Integrate data from multiple sources (APIs, databases, third-party systems).
- Implement data transformation and validation processes.
- Automate data workflows and monitoring.
Data Warehouse Architecture:
- Design and implement a modern data warehouse (AWS Redshift preferred).
- Define data models (star schema, dimensional modeling).
- Optimize performance and cost efficiency.
- Ensure security and access control best practices.
AI & Advanced Analytics:
- Utilize AI/ML tools for demand forecasting, trend analysis, anomaly detection, and build predictive models to support business decisions.
- Explore automation opportunities using AI-driven insights.
Business Data Analysis:
- Analyze operational and financial data to identify growth opportunities.
- Provide insights on revenue, cost optimization, and performance trends.
- Work proactively to uncover hidden patterns and strategic improvements.
