Wild Blossom Meadery
Job Description
Job Description
We are seeking a skilled and detail-oriented
Data Engineer
to join our growing data team. In this role, you will design, build, and maintain the data infrastructure and pipelines that power our analytics, reporting, and machine learning initiatives. You will work closely with data analysts, scientists, and other engineers to ensure reliable, scalable, and high-quality data is available across the organization.
Key Responsibilities
Design, develop, and maintain scalable data pipelines for batch and real-time data processing.
Build and manage ETL/ELT processes to extract data from various sources, transform it, and load it into data warehouses or data lakes.
Collaborate with cross-functional teams to understand data needs and deliver clean, structured, and optimized datasets.
Ensure data quality, integrity, and security through validation, monitoring, and auditing tools.
Optimize performance of data infrastructure, including query tuning, indexing, and storage solutions.
Support the integration of third-party data systems and APIs.
Document data pipelines, schemas, and workflows clearly and thoroughly.
Stay updated on new technologies and best practices in data engineering and analytics.
Required Qualifications
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
2+ years of experience as a Data Engineer or similar role.
Strong proficiency in SQL and one or more programming languages (e.g., Python, Java, Scala).
Experience with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).
Familiarity with data pipeline orchestration tools (e.g., Airflow, dbt, Luigi).
Experience with cloud platforms (e.g., AWS, GCP, Azure).
Understanding of data modeling, data governance, and best practices for data quality.
Preferred Qualifications
Experience with real-time data streaming (e.g., Kafka, Kinesis).
Knowledge of CI/CD and version control tools (e.g., Git, Jenkins).
Exposure to data security and compliance (e.g., GDPR, HIPAA).
Familiarity with machine learning workflows and support infrastructure.
We are seeking a skilled and detail-oriented
Data Engineer
to join our growing data team. In this role, you will design, build, and maintain the data infrastructure and pipelines that power our analytics, reporting, and machine learning initiatives. You will work closely with data analysts, scientists, and other engineers to ensure reliable, scalable, and high-quality data is available across the organization.
Key Responsibilities
Design, develop, and maintain scalable data pipelines for batch and real-time data processing.
Build and manage ETL/ELT processes to extract data from various sources, transform it, and load it into data warehouses or data lakes.
Collaborate with cross-functional teams to understand data needs and deliver clean, structured, and optimized datasets.
Ensure data quality, integrity, and security through validation, monitoring, and auditing tools.
Optimize performance of data infrastructure, including query tuning, indexing, and storage solutions.
Support the integration of third-party data systems and APIs.
Document data pipelines, schemas, and workflows clearly and thoroughly.
Stay updated on new technologies and best practices in data engineering and analytics.
Required Qualifications
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
2+ years of experience as a Data Engineer or similar role.
Strong proficiency in SQL and one or more programming languages (e.g., Python, Java, Scala).
Experience with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).
Familiarity with data pipeline orchestration tools (e.g., Airflow, dbt, Luigi).
Experience with cloud platforms (e.g., AWS, GCP, Azure).
Understanding of data modeling, data governance, and best practices for data quality.
Preferred Qualifications
Experience with real-time data streaming (e.g., Kafka, Kinesis).
Knowledge of CI/CD and version control tools (e.g., Git, Jenkins).
Exposure to data security and compliance (e.g., GDPR, HIPAA).
Familiarity with machine learning workflows and support infrastructure.