Data Engineer
The role involves designing, building, and optimizing scalable data pipelines on Databricks and Snowflake, using Spark, Python, and SQL for ETL/ELT processes. You will collaborate with senior engineers and architects to improve data platform performance and support analytics teams.
Skills required
About this role
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.
