Description

Key Responsibilities · Design, develop, and maintain scalable data pipelines supporting AI/ML and analytics workloads. · Build and optimize batch and real-time data ingestion frameworks. · Develop data integration solutions across multiple internal and external data sources. · Engineer reliable datasets and feature stores that support machine learning model development. · Implement data transformation, cleansing, enrichment, and validation processes. · Design and maintain modern data lake, lakehouse, and data warehouse architectures. · Ensure data quality, integrity, security, and governance standards are met. · Optimize data processing performance, scalability, and operational efficiency. · Collaborate with Data Scientists and Analysts to support feature engineering and model deployment requirements. · Enable MLOps and ML platform capabilities to support model operationalization. · Implement monitoring, observability, and operational support processes for data platforms. · Maintain documentation, data lineage, and metadata management standards. Required Qualifications · Bachelor's degree in Computer Science, Engineering, Information Systems, Data Engineering, or related field. · 4+ years of experience in Data Engineering, Data Platforms, or Analytics Engineering. · Strong proficiency in Python, SQL, Spark, Scala, or equivalent technologies. · Experience building cloud-native data solutions using Azure, AWS, or GCP. · Experience with ETL/ELT frameworks and large-scale data processing. · Knowledge of distributed data processing technologies and modern data architectures. · Experience with data lake, warehouse, and lakehouse platforms. · Understanding of data governance, security, and data quality practices

Key Skills

Education

Bachelor's degree