- Bachelors or Masters degree in Computer Science, Engineering, or related field.

- 3+ years experience as a data engineer or relevant role.

- Understanding of Big Data technologies, DataLake, DeltaLake, Lakehouse architectures.

- Advanced proficiency in Python, SQL, and Apache Spark.

- Deep understanding of data modeling, ETL/ELT processes, and data warehousing concepts.

- Experience with cloud data platforms such as Azure Data Factory, AWS Glue, or GCP Dataflow.

- Strong background in performance tuning and handling large-scale datasets.

- Familiarity with version control, CI/CD pipelines, and agile development practices.

- Proficient with handling different file formats : JSON, Avro, and Parquet.

- Knowledge of one or more database technologies (e.g. PostgreSQL, Redshift, Snowflake, NoSQL databases).

Preferred Qualifications :

- Cloud certification from a major provider (AWS, Azure, or GCP).

- Hands-on experience with Snowflake, Databricks, Apache Airflow, or Terraform.

- Exposure to data governance, observability, and cost optimization frameworks.

Key Responsibilities :

- Design, develop, and maintain scalable data pipelines using Python, SQL, and Apache Spark

- Implement and manage ETL/ELT workflows for structured and unstructured data

- Build and optimize data lake and lakehouse architectures using Delta Lake and related technologies

- Develop and manage cloud-based data solutions using Azure Data Factory, AWS Glue, or GCP Dataflow

- Perform performance tuning and optimization for large-scale data processing

- Ensure data quality, reliability, and consistency across data platforms

- Work with various data formats including JSON, Avro, and Parquet

- Collaborate with cross-functional teams in an Agile development environment

- Implement best practices for version control, CI/CD pipelines, and data engineering workflows