Data Engineer Instructor

Job Description About the Role We are seeking a highly skilled Data & AI Instructor who can train working professionals and collaborate directly with clients to understand their business requirements and deliver customized data-driven solutions. The ideal candidate will have strong hands-on expertise in data science, AI, and data engineering , along with the ability to communicate effectively with corporate teams and adapt content for professional learners. Key Responsibilities Deliver impactful training sessions for corporate professionals on Data Science, Machine Learning, Artificial Intelligence, and Data Engineering topics. Work closely with clients to identify business needs, design relevant technical learning programs, and support solution implementation. Lead corporate training programs, workshops, and consulting sessions tailored for professionals in the data and AI domain. Take ownership of client engagements , including requirement gathering, project understanding, and technical delivery. Develop and update curriculum content, case studies, and hands-on projects that align with real-world business scenarios. Mentor professionals on applying data-driven strategies to solve organizational challenges. Support clients in implementing and optimizing data pipelines, AI models, and analytics workflows . Stay current with emerging technologies in AI, Big Data, Cloud, and Data Engineering to ensure up-to-date training and consulting practices. Requirements Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field . 2–3 years of experience in Data Science, Data Engineering, or AI consulting/training roles. Proven experience in corporate training or client-facing roles . Strong proficiency in: Programming: Python, SQL Data Science Libraries: NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch Data Engineering Tools: Apache Spark, Airflow, Kafka, or similar frameworks Databases: MySQL, PostgreSQL, MongoDB, or equivalent data stores Cloud Platforms: AWS, Azure, or Google Cloud (S3, BigQuery, Redshift, Dataflow) Visualization Tools: Power BI, Tableau, Matplotlib, Seaborn Deep understanding of ETL/ELT pipelines, data warehousing, and machine learning model deployment . Excellent communication and presentation skills for professional and executive-level audiences . Ability to manage multiple client engagements and deliver training outcomes on time. Preferred Qualifications Experience in corporate training, consulting, or professional development programs . Exposure to MLOps , AI model deployment , and CI/CD for ML pipelines . Knowledge of data governance, data quality, and compliance frameworks . Professional certifications such as AWS Data Engineer, Azure Data Scientist, or Google Cloud AI Engineer . Benefits What We Offer Competitive compensation with performance-based incentives. Opportunities for professional growth and certification sponsorship . Work with leading corporate clients and professional learners across industries. Collaborative and innovative environment that values practical application and impact-driven learning . Requirements Educational Background: Bachelor's and Master’s degrees in Data Science, Computer Science, Statistics, or a related field. Technical Skills: Proficient in Python and familiar with key data science libraries (Pandas, Scikit-Learn, TensorFlow, or PyTorch). Strong understanding of all complex machine learning algorithms not limited to decision trees, random forests, and gradient boosting machines. Competence in data preprocessing, cleaning, and analysis. Familiarity with data cleaning, transformation, and preprocessing techniques. Experience with SQL and possibly some NoSQL databases for data querying and manipulation. Basic knowledge of data visualization tools like Matplotlib and Seaborn. Strong skills in SQL for data extraction, and the ability to work with complex database systems. Advanced knowledge of analytical tools and software such as Excel, Tableau, or more specialized software depending on the industry (e.g., SAS, SPSS). Experience with data visualization and the ability to create interactive dashboards. Vast knowledge of NLP, Deep Learning, Machine Learning , Knowledge of genAI with implementation knowledge (LLM, fine tuning RAG implementation) and many more. Familiarity with cloud services (AWS, Azure, Google Cloud) for data processing and storage.. Professional Experience: 2-3 years of experience in a data science or related role. Proven track record of developing and deploying machine learning models to solve business problems. Experience in projects that involve complex data structures and large-scale datasets. Exposure to model validation and implementation in a production environment. Demonstrated experience in analyzing large datasets and delivering actionable insights. Proven track record of effectively communicating findings to help inform business decisions. Experience in performing statistical analysis, forecasting, and establishing data structures that optimize analytical capabilities. Soft Skills: Strong problem-solving skills with a capability to work through complex issues using a logical and analytical approach. Good communication skills to effectively articulate insights and technical details to non-technical stakeholders. Ability to work collaboratively in team settings and manage project timelines effectively. Strong attention to detail with the capability to work on multiple projects simultaneously. Effective communication skills, capable of presenting complex information in an understandable and compelling manner. Collaboration skills to work effectively with both technical teams and business units.