Full Stack Data Engineer

KiE Square is seeking a highly skilled Senior Full Stack Data Engineer with a strong background in analytics consulting. The ideal candidate must have hands-on experience designing and implementing robust data architectures, building scalable data pipelines, and enabling analytics and machine learning solutions for business impact.

This role requires a blend of technical expertise, solution design capability, and business understanding to support analytical consulting projects across diverse industries. You will work closely with data scientists, BI developers, and client teams to ensure high-quality, production-ready data environments and insights delivery.

Key Responsibilities

• Design, develop, and maintain end-to-end data pipelines from multiple data sources (structured & unstructured).
• Build and optimise data architectures, ETL/ELT processes, and data models to support analytics and ML initiatives.
• Collaborate with data scientists and analysts to operationalise predictive and prescriptive solutions.
• Develop APIs and microservices for data consumption and integration with analytics applications.
• Partner with consulting teams to translate business problems into scalable data solutions.
• Implement data quality, governance, and security best practices.
• Drive cloud data migration and modernisation initiatives (AWS, Azure, or GCP).
• Lead performance optimisation and monitoring of data infrastructure.
• Mentor junior engineers and contribute to data engineering best practices within the team.

Required Skills & Qualifications

• Education: Bachelors or Masters degree in Computer Science, Information Systems, Engineering, or related field.
• Experience: 57 years of hands-on experience in data engineering, preferably in an analytics consulting environment.
• Strong proficiency in Python, SQL, and data pipeline frameworks (e.g., Airflow, Prefect, or Luigi).
• Familiarity with API development (Flask/FastAPI) and containerization (Docker, Kubernetes).
• Expertise with data warehousing (Snowflake, Redshift, Big Query, Databricks, or Synapse).
• Hands-on experience with cloud platforms – AWS / Azure / GCP (any one is mandatory).
• Deep understanding of ETL/ELT design patterns, data modelling (Star/Snowflake), and schema design.
• Working knowledge of big data technologies (Spark, Hadoop, Kafka).
• Exposure to CI/CD pipelines, Git-based workflows, and MLOps/DataOps practices.
• Strong problem-solving ability and client communication skills.