We are seeking a skilled and motivated Data Engineer to join our dynamic team. The ideal candidate will have extensive experience in building and optimizing data pipelines, handling large datasets, and working within cloud-based and on-premise environments.
You will be responsible for designing and implementing efficient, scalable solutions to manage, process, and analyze high volumes of data, ensuring the reliability and performance of our data infrastructure.
What will your job look like:
Design, build, and maintain scalable data pipelines to process large datasets efficiently
Develop and implement data models and architectures that support both real-time and batch data processing
Ensure data integrity, security, and accuracy across all systems
Collaborate with data scientists, analysts, and other engineers to ensure data availability and quality
Optimize data retrieval and storage processes to handle large volumes of data seamlessly
Work with structured, semi-structured, and unstructured data, integrating various data sources
Troubleshoot and resolve data issues, ensuring continuous operation of the data infrastructure
Maintain and enhance ETL processes, ensuring scalability and performance in handling large datasets
Stay up-to-date with industry best practices and emerging technologies related to big data engineering
You will be responsible for designing and implementing efficient, scalable solutions to manage, process, and analyze high volumes of data, ensuring the reliability and performance of our data infrastructure.
What will your job look like:
Design, build, and maintain scalable data pipelines to process large datasets efficiently
Develop and implement data models and architectures that support both real-time and batch data processing
Ensure data integrity, security, and accuracy across all systems
Collaborate with data scientists, analysts, and other engineers to ensure data availability and quality
Optimize data retrieval and storage processes to handle large volumes of data seamlessly
Work with structured, semi-structured, and unstructured data, integrating various data sources
Troubleshoot and resolve data issues, ensuring continuous operation of the data infrastructure
Maintain and enhance ETL processes, ensuring scalability and performance in handling large datasets
Stay up-to-date with industry best practices and emerging technologies related to big data engineering
Requirements:
Bachelors degree in Computer Science, Engineering, or a related field
At least 4 years experience in data engineering, with a focus on large-scale data processing and big data technologies
Strong proficiency in Python
Experience with data pipeline and workflow management tools
Hands-on experience with large-scale data processing frameworks like Apache Spark, Hadoop, or similar
Familiarity with data modeling, ETL processes, and data warehousing concepts like Table formats. Apache Iceberg, or similar
Good knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., Cassandra, MongoDB)
Experience with AWS-based data lakes, including working with Amazon S3 for storage, querying data using Amazon Athena, and managing datasets stored in Parquet format
Significant ownership ability
Nice to have:
Knowledge of machine learning frameworks and integrating data pipelines for model training and deployment
Experience with version control systems (e.g., Git), CI/CD pipelines, and automation tools
Experience with containerization (Docker, Kubernetes)
Bachelors degree in Computer Science, Engineering, or a related field
At least 4 years experience in data engineering, with a focus on large-scale data processing and big data technologies
Strong proficiency in Python
Experience with data pipeline and workflow management tools
Hands-on experience with large-scale data processing frameworks like Apache Spark, Hadoop, or similar
Familiarity with data modeling, ETL processes, and data warehousing concepts like Table formats. Apache Iceberg, or similar
Good knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., Cassandra, MongoDB)
Experience with AWS-based data lakes, including working with Amazon S3 for storage, querying data using Amazon Athena, and managing datasets stored in Parquet format
Significant ownership ability
Nice to have:
Knowledge of machine learning frameworks and integrating data pipelines for model training and deployment
Experience with version control systems (e.g., Git), CI/CD pipelines, and automation tools
Experience with containerization (Docker, Kubernetes)
This position is open to all candidates.
