Senior Data Analyst

Job Description

Key Responsibilities ETL Development & AI Data Pipelines: Design and develop ETL processes to extract, transform, and load data from various sources for ML model training and inference Create data pipelines that can efficiently handle structured and unstructured data for AI applications Work with data scientists to implement feature engineering transformations at scale Machine Learning Operations: Assist in developing automated ML workflows for model training, evaluation, and deployment Implement basic model monitoring and observability solutions Create scripts to automate data preparation for ML model development Python Development: Write efficient Python scripts leveraging data science and ML libraries (pandas, scikit-learn, etc.) Develop reusable code modules for data processing and basic ML operations Maintain and optimize existing Python codebases Automation & Orchestration: Build automation frameworks to streamline ML model deployment and monitoring Implement data validation tests to ensure data quality for ML models Create workflow orchestration solutions for end-to-end ML pipelines Collaboration: Work closely with data scientists to understand model requirements and implementation needs Collaborate with cross-functional teams to integrate ML solutions into business applications Participate in code reviews and knowledge sharing sessions Qualifications Education: Bachelor's degree in Computer Science, Data Science, Information Technology, or related field Relevant certifications in data engineering, machine learning, or cloud technologies are a plus Experience: 1-2 years of experience in ETL development, Python programming, and data engineering Exposure to machine learning workflows and basic ML operations Experience with ETL tools such as Apache NiFi, Talend, or similar platforms Technical Skills: Proficiency in Python with experience using data processing libraries (pandas, NumPy) Knowledge of ML libraries like scikit-learn, TensorFlow, or PyTorch Experience with SQL and relational databases Basic understanding of cloud data services (AWS, Azure, or GCP) Familiarity with version control systems (Git) and CI/CD concepts Understanding of data quality practices for ML applications Soft Skills: Strong problem-solving abilities and analytical mindset Good communication skills and ability to work in cross-functional teams Self-motivated with eagerness to learn new technologies Primary Skill Set ETL & Data Pipeline Development: 1-2 years Designing and implementing data pipelines for analytics and ML Experience with ETL tools and data integration patterns Ability to handle various data formats and sources Python Programming & ML Libraries: 1-2 years Proficiency in Python with focus on data manipulation and processing Experience with pandas, NumPy, and scikit-learn Basic knowledge of ML frameworks (TensorFlow/PyTorch) Machine Learning Operations: 0-1 year Basic understanding of ML model deployment workflows Experience with model serving and monitoring Knowledge of feature engineering techniques Data Management for AI Applications: 1-2 years Experience with datasets preparation for machine learning Knowledge of data quality and validation techniques Understanding of data versioning and lineage tracking Automation & Orchestration: 1-2 years Experience building automated workflows for data processing Knowledge of workflow orchestration tools (Airflow, Prefect, etc.) Ability to implement testing frameworks for data pipelines