Data Engineer

This role is for one of the Weekday's clients Salary range: Rs 1500000 - Rs 2500000 (ie INR 15 - 25 LPA) Min Experience: 3 years Location: Kolkata JobType: full-time We are seeking a skilled and motivated Data Engineer with a strong foundation in data engineering and exposure to machine learning workflows. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines while enabling data-driven decision-making and supporting ML model development. You will work closely with data scientists, analysts, and engineering teams to ensure high-quality, reliable, and efficient data systems.

Requirements Key Responsibilities: Design, develop, and maintain robust, scalable, and efficient data pipelines for processing large volumes of structured and unstructured data. Build and optimize ETL/ELT workflows to ensure timely and accurate data availability across systems. Collaborate with data scientists and ML engineers to prepare datasets for training, validation, and production deployment of machine learning models.

Implement data validation, cleansing, and monitoring processes to ensure data quality, integrity, and consistency. Develop and manage data storage solutions using modern data platforms such as data lakes and data warehouses. Optimize data infrastructure for performance, scalability, and cost-efficiency in cloud environments.

Enable real-time and batch data processing systems using appropriate technologies. Assist in deploying and maintaining machine learning pipelines, including feature engineering and model monitoring. Ensure data security, governance, and compliance with best practices and organizational standards.

Document data architecture, workflows, and processes for knowledge sharing and maintainability. Required Skills & Qualifications: 3–5 years of hands-on experience in data engineering or related roles. Strong proficiency in programming languages such as Python, Scala, or Java.

Experience with SQL and NoSQL databases, and strong understanding of data modeling concepts. Practical experience with big data technologies like Apache Spark, Hadoop, or similar frameworks. Familiarity with cloud platforms such as AWS, Google Cloud, or Azure, including data services (e.g., S3, BigQuery, Redshift, or Azure Data Lake).

Experience building and maintaining ETL/ELT pipelines using tools like Airflow, dbt, or similar orchestration frameworks. Solid understanding of machine learning workflows, including data preprocessing, feature engineering, and model lifecycle support. Exposure to ML frameworks such as TensorFlow, PyTorch, or Scikit-learn is a plus.

Knowledge of streaming technologies like Kafka or Kinesis is desirable. Strong problem-solving skills and the ability to work with large, complex datasets. Preferred Qualifications: Experience working in cross-functional teams involving data science and product engineering.

Understanding of MLOps principles and tools for model deployment and monitoring. Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes. Experience with version control systems like Git and CI/CD pipelines.