We are looking for a skilled and proactive Mid-Level Data Engineer to design, build, and maintain scalable data infrastructure and pipelines. With 2–4 years of experience, the ideal candidate will work independently on data engineering tasks, optimize workflows, and collaborate cross-functionally to support analytical and operational data needs across the organization.
2–4 years of hands-on experience in data engineering or software engineering with a focus on data-intensive applications
Proven experience building ETL/ELT pipelines and working with large-scale data systems
Design, develop, and maintain robust, scalable, and efficient batch and streaming data pipelines
Implement and optimize ETL/ELT workflows to process data from diverse sources
Collaborate with analysts, data scientists, and product teams to define data requirements and deliver solutions
Manage data ingestion, transformation, and integration across various platforms
Ensure data quality, reliability, and consistency through validation and monitoring processes
Contribute to data modeling efforts and maintain logical and physical data models
Document data flows, pipeline architecture, and data-related best practices
Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related technical discipline
Master’s degree is a plus
Strong proficiency in SQL and experience working with relational and columnar databases (e.g., PostgreSQL, Snowflake, Redshift)
Proficient in Python (or similar) for scripting and data manipulation
Experience with cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure
Familiarity with data pipeline orchestration tools (e.g., Apache Airflow, Luigi, Prefect)
Understanding of data warehousing and data lake architectures
Knowledge of data modeling techniques, including dimensional and normalized models
Hands-on experience with version control tools (e.g., Git), and working in agile environments
Familiarity with big data tools like Spark, Kafka, or Hive is a plus
Exposure to CI/CD practices for data pipeline deployment and testing