We are looking for a passionate and highly motivated Data Engineer to join our growing data team. In this role, you will work on building scalable data platforms, optimizing large-scale data pipelines, and enabling data-driven decision-making across the organization. You will collaborate closely with Data Scientists, Analysts, and business stakeholders to develop modern cloud-based data solutions using technologies such as Databricks, Snowflake, PySpark, SQL, and Python. If you enjoy solving complex data challenges and working in a fast-paced, innovative environment, we’d love to connect with you.
Key Responsibilities:
Design, build, and maintain scalable ETL/ELT pipelines for processing large volumes of structured and unstructured data
Develop high-performance data processing solutions using PySpark and distributed computing frameworks
Build, optimize, and manage data platforms on Databricks and/or Snowflake
Write clean, efficient, and production-ready SQL queries and Python code for data transformation, automation, and analytics
Collaborate with cross-functional teams including Data Analysts, Data Scientists, Product teams, and Business stakeholders to deliver data-driven solutions
Ensure data quality, governance, integrity, scalability, and reliability across enterprise data systems
Monitor, troubleshoot, and optimize existing pipelines, workflows, and database performance
Implement best practices around coding standards, testing, CI/CD, version control, and technical documentation
Required Skills & Qualifications:
2–5 years of experience in Data Engineering or related roles
Strong hands-on experience with Databricks and/or Snowflake
Proficiency in SQL and Python programming
Practical experience with PySpark and distributed data processing
Solid understanding of Data Warehousing, ETL/ELT concepts, and Data Modeling
Experience working with large-scale datasets in cloud-based environments
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field
Preferred Skills:
Experience with cloud platforms such as AWS, Azure, or GCP
Familiarity with orchestration and transformation tools such as Airflow, dbt, or Azure Data Factory (ADF)
Knowledge of Git, CI/CD pipelines, and DevOps best practices
Exposure to Delta Lake, Lakehouse architecture, Kafka, Spark Streaming, or real-time data processing
Experience working in Agile/Scrum environments is a plus