Senior Data Engineer
New
A
Aimpoint DigitalData analytics
Full-time, remote work within ColombiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years working with relational databases and query languages; 3+ years building data pipelines in production; 3+ years data modeling; 3+ years writing clean, maintainable, and robust code.
- Required Skills
- PythonSQLJavaSparkScalaData modelingdbt
Requirements
- Hold a degree in Computer Science, Engineering, or Mathematics, or have equivalent experience.
- Have experience managing stakeholders and collaborating with customers.
- Have 3+ years working with relational databases and query languages.
- Have 3+ years building production data pipelines and working across structured, semi-structured, and unstructured data.
- Have 3+ years of data modeling experience, such as star schema or entity-relationship modeling.
- Have 3+ years writing clean, maintainable, robust code in Python, Scala, Java, or similar languages.
- Be able to manage an individual workstream independently.
- Bring expertise in software engineering concepts and best practices.
- Be willing to travel.
- Preferred: DevOps experience and experience with cloud data warehouses such as Snowflake, Google BigQuery, AWS Redshift, or Microsoft Synapse.
- Preferred: experience with cloud ETL/ELT tools such as Fivetran, dbt, Matillion, Informatica, or Talend.
- Preferred: experience with AWS, Azure, or GCP; Docker or Kubernetes; Apache Spark; and preparing data for analytics using a data science workflow.
- Consulting experience is strongly preferred.
Responsibilities
- Build trusted-advisor relationships with client data owners, analytic users, and C-level executives.
- Work independently as part of a small team to solve data engineering use cases across industries.
- Design and develop analytical layers, cloud data warehouses, and data lakes.
- Build ETL/ELT pipelines and orchestration jobs.
- Write code in SQL, Python, and Spark and apply software engineering best practices such as Git and CI/CD.
- Support deployment of data science and machine learning projects into production; the role does not develop ML models or algorithms.
- Contribute to practice development, business development, and company initiatives.
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