Data Engineer _ Python & Airflow _ Remote

Key role responsibilities:

• Develop and implement data pipelines and systems to connect and process data for

analytics and business intelligence (BI) platforms.

• Document systems and source-to-target mappings to ensure transparency and a clear

understanding of data flow.

• Re-engineer manual data flows to enable scalability, automation, and efficiency for

repeatable use.

• Adhere to and contribute to best practice guidelines, continuously striving for

optimization and improvement.

• Write clean, secure, and well-tested code, ensuring reliability, maintainability, and

compliance with development standards.

• Monitor and operate the services and pipelines you build, proactively identifying and

resolving production issues.

• Assess and prioritize feature requests based on business needs, technical feasibility,

and impact.

• Identify opportunities to optimize existing data flows, promoting efficiency and reducing

redundancy.

• Collaborate closely with team members and stakeholders to align efforts and achieve

shared objectives.

• Implement data quality checks and validation processes to ensure accuracy and resolve

data inconsistencies.

Requirements and Skills:

• Strong background in Software Engineering, with proficiency in Python development (3+

years of experience).

• Excellent problem-solving, communication, and organizational skills.
• Ability to work independently and collaboratively within a team environment.
• Understanding of industry-recognized data modelling patterns and standards, and their

practical application.

• Familiarity with data security and privacy principles, ensuring compliance with

governance and regulatory requirements.

• Proficiency in SQL, with experience in PostgreSQL database management.
• Experience in API implementation and integration, with an understanding of REST

principles and best practices.

• Knowledge of validation libraries like Marshmallow or Pydantic.
• Expertise in Pandas, Polars, or similar libraries for data manipulation and analysis.
• Proficiency in workflow orchestration tools like

  Apache Airflow

  and Dagster, ensuring

efficient data pipeline scheduling and execution.

• Experience working with Apache Iceberg, enabling optimized data management and

storage within large-scale analytics environments.

• Understanding of data lake architectures, leveraging scalable storage solutions for

structured and unstructured data.

• Familiarity with data warehouse solutions, ensuring efficient data processing, query

performance, and analytics workflows.

• Knowledge of operating systems (Linux) and modern development practices, including

infrastructure deployment (DevOps).

• Proficiency in code versioning tools such as Git/GitHub, and experience with CI/CD

pipelines (e.g., CircleCI).