Data Engineer

$ 5,000 - $ 7,500 / month

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Responsibilities:

Design, build, and maintain scalable and efficient data pipelines for collecting, transforming, and loading data (ETL processes).
Integrate data from various sources, including APIs, databases, and third-party services into a centralized data warehouse.
Ensure the data is stored efficiently and securely in databases or cloud-based storage solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake).
Ensure the data pipelines are optimized for performance and scalability.
Monitor data quality, resolve issues, and implement data validation processes to ensure consistency and accuracy.
Work with data scientists, analysts, and business stakeholders to ensure data is accessible and valuable for analysis.
Automate repetitive tasks and improve data workflow efficiency.
Implement and ensure adherence to data security practices, compliance, and governance protocols.
Document processes, pipelines, and data workflows for transparency and knowledge sharing.

Requirements:

Degree in Computer Science, Information Technology, Data Science, or a related field, or equivalent practical experience.
2-5 years of experience in a data engineering role or similar.
Strong SQL skills for querying and managing relational databases.
Experience with programming languages such as Python, Java, or Scala to build and maintain data pipelines.
Experience with ETL tools like Apache Airflow, Talend, or custom-built solutions.
Familiarity with big data tools and frameworks such as Hadoop, Spark, Kafka, or similar.
Knowledge of cloud-based data storage and services such as AWS, Google Cloud, or Azure.
Experience with data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake).
Proficiency with Git or similar version control systems.
Understanding of data modeling principles for organizing and structuring data in databases.
Strong troubleshooting and debugging skills to identify and resolve issues with data pipelines and architecture.
Ability to communicate effectively with technical and non-technical teams.
Understanding of how to integrate data pipelines with machine learning models preferred.
Familiarity with data privacy, security policies, and compliance requirements (e.g., GDPR, HIPAA) preferred.

Please send your detailed resume in MS Word format to with

Responsibilities:

Design, build, and maintain scalable and efficient data pipelines for collecting, transforming, and loading data (ETL processes).
Integrate data from various sources, including APIs, databases, and third-party services into a centralized data warehouse.
Ensure the data is stored efficiently and securely in databases or cloud-based storage solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake).
Ensure the data pipelines are optimized for performance and scalability.
Monitor data quality, resolve issues, and implement data validation processes to ensure consistency and accuracy.
Work with data scientists, analysts, and business stakeholders to ensure data is accessible and valuable for analysis.
Automate repetitive tasks and improve data workflow efficiency.
Implement and ensure adherence to data security practices, compliance, and governance protocols.
Document processes, pipelines, and data workflows for transparency and knowledge sharing.

Requirements:

Degree in Computer Science, Information Technology, Data Science, or a related field, or equivalent practical experience.
2-5 years of experience in a data engineering role or similar.
Strong SQL skills for querying and managing relational databases.
Experience with programming languages such as Python, Java, or Scala to build and maintain data pipelines.
Experience with ETL tools like Apache Airflow, Talend, or custom-built solutions.
Familiarity with big data tools and frameworks such as Hadoop, Spark, Kafka, or similar.
Knowledge of cloud-based data storage and services such as AWS, Google Cloud, or Azure.
Experience with data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake).
Proficiency with Git or similar version control systems.
Understanding of data modeling principles for organizing and structuring data in databases.
Strong troubleshooting and debugging skills to identify and resolve issues with data pipelines and architecture.
Ability to communicate effectively with technical and non-technical teams.
Understanding of how to integrate data pipelines with machine learning models preferred.
Familiarity with data privacy, security policies, and compliance requirements (e.g., GDPR, HIPAA) preferred.

Please send your detailed resume in MS Word format to with