Data Engineer
| Company | MEMIC |
|---|---|
| Location | Portland, ME, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Mid Level, Senior |
Requirements
- A minimum of four years of experience with data engineering, database development or related experience is required.
- Experience working in both AWS and Azure cloud environments.
- Experience in Databricks.
- Familiarity with big data technologies such as Spark, Apache, and Hadoop.
- Knowledge of database design, data modeling, and data warehousing concepts.
- Proficiency in SQL and experience working with relational and non-relational databases.
- Familiarity with cloud databases, such as CosmosDB and AWS, and cloud data warehouses and lakes, such as Snowflake.
- Experience with data integration tools and ETL processes.
- Proficiency in programming languages such as Python, Java, or Scala.
- Strong analytical and problem-solving skills with the ability to identify and address complex data-related issues.
- Attention to detail and a commitment to ensuring data quality and accuracy.
Responsibilities
- Design, build, and maintain efficient and scalable data pipelines to process structured and unstructured data from multiple sources.
- Work closely with the data architect to design and maintain a scalable and efficient data architecture that supports the organization’s data needs.
- Assist in the development of data models and schemas to support analytical and reporting requirements.
- Integrate data from diverse sources, including databases, APIs, and streaming data, to provide a unified view of our data.
- Collaborate with software engineers, analysts, and other stakeholders to understand data requirements and implement data solutions that meet business needs.
- Ensure data quality, integrity, and accuracy by implementing data validation, monitoring, and testing processes.
- Optimize data storage, processing, and retrieval mechanisms to improve performance and reduce costs.
- Develop and maintain documentation related to data engineering processes, governance, best practices, systems, and tools.
- Identify opportunities for data-related process improvements and work with cross-functional teams to implement them.
- Stay updated with emerging trends and technologies in data engineering and adopt best practices to continuously improve the data infrastructure.
Preferred Qualifications
-
No preferred qualifications provided.