Data Engineer

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Qualifications

• Candidates should possess strong Data Engineering and Data Warehousing skills for building and maintaining scalable data infrastructure.
• Candidates should possess Data Modeling skills to design efficient, well-structured schemas that support analytics and product needs.
• Candidates should possess Extract Transform Load (ETL) skills to create and optimize reliable data pipelines from multiple sources.
• Candidates should possess Data Analytics skills to interpret data, support reporting, and collaborate on insight-driven decisions.
• Proficiency in SQL and at least one programming language commonly used in data engineering (such as Python or Scala).
• Experience with cloud data platforms and services (such as AWS, Azure, or GCP) and modern data tools (for example, Spark, dbt, or Airflow).
• Strong problem-solving, documentation, and communication skills, with the ability to collaborate in a remote, distributed environment.
• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience.

• Required Qualifications
• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
• Experience in data engineering, data integration, or a related technical role.
• Strong proficiency in SQL and experience working with relational databases.
• Programming experience in Python, Java, Scala, or a similar language.
• Hands-on experience developing ETL/ELT pipelines and data transformation workflows.
• Knowledge of data warehousing, data modeling, and database design principles.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with data processing frameworks and orchestration tools.
• Strong analytical, problem-solving, and troubleshooting skills.
• Excellent communication and collaboration skills.
• Preferred Qualifications
• Experience with platforms such as Snowflake, Databricks, Google BigQuery, Amazon Redshift, or Azure Synapse Analytics.
• Familiarity with Apache Spark, Apache Kafka, or other distributed data processing technologies.
• Experience with workflow orchestration tools such as Apache Airflow, Azure Data Factory, or AWS Glue.
• Knowledge of data lake and lakehouse architectures.
• Experience with version control systems such as Git and CI/CD practices.
• Familiarity with data governance, metadata management, and data observability tools.
• Experience working in Agile or cross-functional engineering teams.