Data Platform Engineer (Coding & ETL Tooling)
- Support the setup and maintenance of development environments using IDEs such as VSCode, RStudio, Cursor, and Jupyter
- Enable best practices for collaborative coding in languages such as Python, R, and Stata
- Ensure integration between IDEs, data platforms, and source control tools for streamlined workflows
- Assist in optimizing development environments for reproducibility, package management, and dependency tracking
- Administer Git-based version control systems (GitHub, GitLab), including branching strategies, access control, and repo management
- Develop and manage CI/CD pipelines using GitLab Runners and GitHub Actions for data pipelines and analytical code
- Promote code quality through automated testing, linting, and review workflows
- Support onboarding and upskilling of users in Git workflows and coding standards
- Design and implement data transformation pipelines using open-source tools like Apache Airflow, dbt, and VTL (Validation and Transformation Language)
- Maintain orchestration workflows and monitor execution of scheduled jobs
- Optimize task dependencies, retries, and performance within Airflow DAGs and dbt models
- Integrate ETL tools with source systems, metadata layers, and data warehouses
- Build reproducible workflows for data science, statistical analysis, and reporting using templated code bases and configuration-driven pipelines
- Develop modular, reusable components for data ingestion, cleaning, validation, and transformation
- Create infrastructure-as-code templates for deploying ETL tools in cloud or on-prem environments
- Support interoperability and standardization across analytics and data engineering teams
Technical Skills
- 6+ years of experience with data scripting and statistical programming languages (Python, R, Stata)
- Strong proficiency with Git-based workflows and tools (GitLab, GitHub, GitHub Actions)
- Experience configuring and working within IDEs such as VSCode, RStudio, Jupyter, and/or Cursor
- Proven track record implementing and managing open-source ETL/ELT tools (Airflow, dbt, GitLab Runners, VTL)
- Familiarity with data orchestration, testing, and observability for pipelines
- Experience developing CI/CD pipelines for analytical and data engineering use cases
- Knowledge of containerization (Docker) and task execution environments (Kubernetes, GitLab Runners)
- Scripting expertise (Bash, Python, YAML) for configuration, automation, and job orchestration
- Understanding of software engineering best practices (modular design, unit testing, reproducibility)
- Bachelors or Masters degree in Data Engineering, Computer Science, Statistics, or related field
- Experience working in collaborative research or analytics teams with reproducible coding standards
- Knowledge of data validation frameworks (e.g., Great Expectations, VTL), metadata integration, and lineage tracking
- Familiarity with cloud-native infrastructure and deployment (AWS, GCP, Azure)
- Contributions to or experience working with open-source ETL/analytics tooling
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