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We are seeking a highly skilled Data Engineer for a permanent role to lead the design and implementation of robust data pipelines and advanced analytics models. In this position, you will be the backbone of our data initiatives, ensuring that internal and external datasets are securely extracted, transformed, and curated to support Artificial Intelligence (AI) and Business Intelligence (BI) across the enterprise. You will bridge the gap between raw data sources and actionable insights by building high-performance semantic layers and feature pipelines.
Job Responsibility:
Design, build, and maintain scalable ETL/ELT pipelines using tools like Fivetran and DBT to integrate diverse internal and external data sources into analytic databases
Model databases for efficient storage and retrieval, ensuring the integration of complex datasets aligns with enterprise architecture standards
Prepare curated tables, semantic layers, and feature pipelines specifically designed for machine learning models and executive reporting
Establish data quality metrics, perform profiling and cleansing, and collaborate with business units to remediate data integrity issues
Contribute to the development of enterprise data architecture and meticulously document data lineage to ensure compliance and transparency
Support critical initiatives related to data classification, loss prevention, and privacy standards to protect the organization’s digital assets
Provide expert guidance to peers and external partners on reporting standards, data organization, and governance
Requirements:
Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative field
Minimum of 4 years in a quantitative or analytics role with a proven track record of managing complex data environments
Proficient in Python, SQL, and Microsoft Azure, with hands-on experience in DBT, Fivetran, Knime, or Dataiku
High proficiency in SQL and experience with at least two relational database management systems (RDBMS)
Demonstrated experience in feature engineering and semantic modeling for AI/BI applications
Familiarity with scripting languages such as R and VBA in addition to Python
Solid understanding of data privacy, classification policies, and data governance frameworks
Strong ability to translate complex technical pipeline concepts into clear, actionable information for non-technical stakeholders