Senior Data Engineer / Data Scientist

• At least 7 years
• The applicants should have experience in the following business area(s): Garments, Multinational Companies

• Bachelor of Science (BSc) in Computer Science

• Bachelor's degree in Data Science, Statistics, Mathematics, Software Engineering, Economics, or a related quantitative field

• At least 7 years
• The applicants should have experience in the following business area(s): Garments, Multinational Companies

• Age At least 28 years

• Strong analytical and critical-thinking skills, with genuine curiosity about complex, ambiguous business problems.

• Ability to design scalable, maintainable, and well-governed data solutions — not just one-off scripts.

• Rigorous attention to data quality, statistical validity, and measurable business impact.

• Ability to communicate technical findings clearly to non-technical stakeholders and influence business decisions.

• A collaborative, ownership-driven mindset and willingness to mentor and learn from peers.

Data Engineering & Platform

•      Design, build, and maintain scalable ETL/ELT pipelines integrating ERP, CRM, APIs, databases, files, IoT, and other enterprise systems.

•      Architect and optimize data warehouses, data lakes, and modern lakehouse platforms, including dimensional and semantic data models.

•      Implement data quality, validation, monitoring, lineage, and governance frameworks across all production data assets.

•      Prepare and curate high-quality datasets — including embeddings and vector stores — for machine learning, predictive analytics, and LLM/RAG applications.

•      Optimize database and pipeline performance for both transactional and analytical workloads at scale.

Data Science & Applied Analytics

•      Analyze structured and unstructured data to uncover trends, patterns, and untapped business opportunities.

•      Design and build machine learning and statistical models for forecasting, classification, recommendation, anomaly detection, and optimization.

•      Conduct rigorous exploratory data analysis, feature engineering, hypothesis testing, and experimental design (A/B testing) to support business decisions.

•      Partner with AI Engineers to transition validated models from research/prototype into production, and monitor their real-world performance.

•      Communicate findings clearly through dashboards, reports, and narratives tailored to both technical and executive audiences.

Business Intelligence & Enablement

•      Develop interactive, decision-ready dashboards and reports using Power BI and Microsoft Fabric.

•      Establish self-service analytics capabilities and data literacy practices across business units.

•      Document data architecture, methodologies, model assumptions, and performance benchmarks for reproducibility and audit readiness.

•      Research and evaluate new data engineering, statistical, and machine learning techniques, and champion their responsible adoption.