Data Engineer

Description 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.