
Data Architect and Engineering Specialist
16 hours ago
Data Engineering Expert
">The role of the Data Engineer is pivotal in designing, implementing, and optimizing data pipelines and architectures to support AI and machine learning initiatives. This involves creating scalable, reliable, and efficient data processing systems.
">Key Responsibilities:
- ">
- Design, build, and maintain scalable data pipelines to extract, transform, and load (ETL) data from various sources, ensuring efficient data processing and analytics needs.">
- Integrate structured and unstructured data from diverse sources, ensuring high data quality, availability, and consistency.">
- Deploy and manage data solutions on cloud platforms, focusing on performance, cost-efficiency, and security.">
- Develop feature engineering techniques to transform raw data into meaningful input for machine learning models.">
- Collaborate with ML engineers and domain experts to annotate and label datasets, ensuring accurate categorization for training LLMs and other AI models.">
- Perform thorough data cleaning and preprocessing tasks, including handling missing values, removing duplicates, and normalizing data to ensure consistency and reliability in data pipelines.">
- Establish and enforce data quality standards, conducting regular audits and validations to maintain dataset integrity and accuracy used for training and inferencing.">
- Work closely with data scientists, ML engineers, software engineers, and stakeholders to ensure project alignment on goals, timelines, and deliverables.">
- Create comprehensive documentation for data pipelines, systems, and processes to ensure knowledge transfer and continuity.">
Requirements:
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- Bachelor's or master's degree in Computer Science, Computer Engineering, Data Science, AI/ML, or a related field.">
- Moderate experience with big data technologies (Hadoop, Spark, Kafka), cloud platforms (e.g., AWS, Azure, GCP), and their data services (e.g., S3, Redshift, BigQuery).">
- Familiarity with automation tools, CI/CD pipelines, and containerization (e.g., Docker, Kubernetes).">
- Experience with natural language processing datasets for NLP and LLM-based applications.">
- Understanding of machine learning concepts and their application to data engineering tasks.">
- Strong proficiency in SQL, Python, and relevant programming languages.">
- Strong understanding of data warehousing, ETL processes, and data modeling.">
- Excellent problem-solving skills and ability to work in a fast-paced environment.">
- Passion for learning and staying updated with emerging technologies.">
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