Data Platform Engineer

About the job Data Platform Engineer (Coding & ETL Tooling)Position OverviewWe are seeking a Data Platform Engineer with a strong background in modern coding environments and open-source ETL/ELT technologies. The successful candidate will support the development, orchestration, and automation of data workflows using tools like Python, R, GitLab Runners, Airflow, and dbt. This role also involves managing and optimizing collaborative development environments (GitHub, GitLab) and supporting IDE usage across data science and engineering teams.Key ResponsibilitiesCoding Environment ManagementSupport the setup and maintenance of development environments using IDEs such as VSCode, RStudio, Cursor, and JupyterEnable best practices for collaborative coding in languages such as Python, R, and StataEnsure integration between IDEs, data platforms, and source control tools for streamlined workflowsAssist in optimizing development environments for reproducibility, package management, and dependency trackingSource Control & CI/CDAdminister Git-based version control systems (GitHub, GitLab), including branching strategies, access control, and repo managementDevelop and manage CI/CD pipelines using GitLab Runners and GitHub Actions for data pipelines and analytical codePromote code quality through automated testing, linting, and review workflowsSupport onboarding and upskilling of users in Git workflows and coding standardsETL/ELT Tooling & OrchestrationDesign 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 jobsOptimize task dependencies, retries, and performance within Airflow DAGs and dbt modelsIntegrate ETL tools with source systems, metadata layers, and data warehousesAutomation & ReproducibilityBuild reproducible workflows for data science, statistical analysis, and reporting using templated code bases and configuration-driven pipelinesDevelop modular, reusable components for data ingestion, cleaning, validation, and transformationCreate infrastructure-as-code templates for deploying ETL tools in cloud or on-prem environmentsSupport interoperability and standardization across analytics and data engineering teamsRequired QualificationsTechnical Skills6+ 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 CursorProven track record implementing and managing open-source ETL/ELT tools (Airflow, dbt, GitLab Runners, VTL)Familiarity with data orchestration, testing, and observability for pipelinesDevOps & Workflow AutomationExperience developing CI/CD pipelines for analytical and data engineering use casesKnowledge of containerization (Docker) and task execution environments (Kubernetes, GitLab Runners)Scripting expertise (Bash, Python, YAML) for configuration, automation, and job orchestrationUnderstanding of software engineering best practices (modular design, unit testing, reproducibility)Preferred QualificationsBachelors or Masters degree in Data Engineering, Computer Science, Statistics, or related fieldExperience working in collaborative research or analytics teams with reproducible coding standardsKnowledge of data validation frameworks (e.g., Great Expectations, VTL), metadata integration, and lineage trackingFamiliarity with cloud-native infrastructure and deployment (AWS, GCP, Azure)Contributions to or experience working with open-source ETL/analytics tooling #J-18808-Ljbffr