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Remote work, popular Data Engineer title, and mid-level profile increase applicant density and competition.
Core data engineering skills are transferable across industries, though geoscience domain preference moderately increases fit sensitivity.
Multiple mandatory technical skills (Python, SQL, Airflow, RDBMS, data modelling) but no explicit years requirement yields moderate strictness.
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Develop and maintain data integrations and connectors between internal and client systems across various industries including geothermal, environmental, hydrocarbon, and mineral exploration.
Contribute to data platform infrastructure including orchestration systems, data processing logic, and modular data transformation frameworks.
Build robust, metadata-driven data pipelines ensuring maintainability, performance optimization, and alignment with downstream application needs; influence architecture and technology choices through documentation and communication.
Proven experience developing data integrations, especially with geoscience or other scientific data types.
Strong proficiency in Python and SQL for secure, performant code and data pipeline optimization.
Experience with orchestration tools like Airflow and significant RDBMS experience (PostgreSQL, Oracle).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in data architecture practices including data modelling, data warehousing, and schema design (3NF, dimensional, medallion).
Familiar with cloud native data technologies (Azure, Azure Data Factory, Databricks) and containerization tools (Docker, Kubernetes).
Able to communicate technical designs to both technical and non-technical stakeholders; comfortable working in open, collaborative, multi-disciplinary teams.