We are looking for passionate and curious Data Engineers who enjoy solving complex data challenges and building modern data platforms. If you love working with large-scale data, designing efficient pipelines, exploring cloud technologies, and continuously learning new tools, this role is for you. Hands-on experience or strong interest in Databricks, Snowflake, Spark, SQL, Python, and cloud-based data engineering is highly desirable. Whether through academic projects, certifications, hackathons, or personal initiatives, we value candidates who demonstrate a genuine passion for data and a drive to build innovative data solutions. Role Overview: As a Databricks & Snowflake Data Engineer, you will work closely with experienced data engineers, architects, and analytics teams to design, build, and optimize modern data platforms. You will be involved in the end-to-end data engineering lifecycle—from data ingestion and transformation to pipeline orchestration, data modeling, and performance optimization. This role offers a hands-on learning environment where you'll work on real-world business challenges, gain exposure to cloud-based data platforms, and develop expertise in modern data engineering technologies including Databricks, Snowflake, Spark, Python, SQL, and cloud ecosystems. ### Responsibilities Responsibilities - Work with senior data engineers and architects to build, optimize, and maintain scalable data pipelines and workflows. - Develop ETL/ELT processes using Databricks, Spark, Python, and SQL. - Design and implement data ingestion frameworks for structured and unstructured data sources. - Build and maintain data models, data marts, and analytical datasets in Snowflake. - Monitor, troubleshoot, and improve data pipeline performance, reliability, and scalability. - Collaborate with business stakeholders, analysts, and data scientists to understand data requirements and deliver solutions. - Participate in architecture discussions, proof-of-concepts, and process improvement initiatives. - Document data flows, technical designs, and implementation details to support operational excellence and knowledge sharing. - Ensure adherence to data quality, security, and governance standards across data platforms. ### Qualifications Qualifications - Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related technical field. - Strong foundation in SQL and database concepts. - Good programming skills in Python or a similar language. - Understanding of data warehousing concepts, ETL/ELT processes, and data modeling. - Familiarity with Databricks, Apache Spark, Snowflake, or cloud data platforms through academic projects, internships, certifications, hackathons, or personal projects. - Basic knowledge of cloud platforms such as Azure, AWS, or GCP is a plus. - Strong analytical and problem-solving skills with a passion for learning modern data engineering technologies. - Excellent communication and collaboration skills.