N-iX is looking for a Junior Data Engineer to join a data-focused team supporting the day-to-day operations and ongoing development of enterprise data solutions. This is a 6-month engagement with an opportunity to gain hands-on experience working with modern cloud data platforms, large-scale datasets, and production data pipelines.
You will work closely with experienced Data Engineers, contributing to pipeline maintenance, troubleshooting, onboarding new data sources, and implementing well-defined enhancements across the data platform. This role is ideal for engineers with 1-2 years of professional experience who want to deepen their expertise in data engineering and cloud technologies.
About the Role
As a Junior Data Engineer, you will help ensure the reliability and quality of data pipelines that support business-critical analytics and reporting. You'll participate in monitoring, troubleshooting, testing, and implementing data processing logic while following established engineering standards and delivery practices.
Under the guidance of senior team members, you will contribute to data ingestion processes, validation activities, technical documentation, and operational support, gaining exposure to modern technologies such as Azure, Databricks, PySpark, and Snowflake.
Responsibilities
- Monitor and support scheduled and event-driven data pipelines, data processing jobs, reporting refreshes, and data quality checks.
- Execute standard data ingestion and refresh activities from multiple sources, including APIs, CSV files, syndicated datasets, and configuration inputs.
- Implement well-defined Python, PySpark, and SQL changes, including new filters, transformations, mappings, validation rules, and minor pipeline enhancements.
- Perform schema validation, reconciliation checks, regression testing, and source-to-target data verification.
- Investigate pipeline failures, validation issues, missing data, rejected files, and reporting discrepancies.
- Troubleshoot first-line operational incidents using logs, job outputs, and monitoring tools.
- Escalate complex technical or business-data issues with clear diagnostic information and supporting evidence.
- Maintain technical documentation, deployment notes, runbooks, configuration files, and operational records.
- Follow established engineering practices, including Git workflows, pull requests, code reviews, testing, and deployment procedures.
- Support onboarding of new data sources and contribute to continuous improvement initiatives.
Requirements
Must Have
- 1-2 years of professional experience in Data Engineering, ETL/ELT development, Analytics Engineering, or Data Support roles.
- Good working knowledge of Python and SQL.
- Understanding of data pipelines, data transformations, batch processing, and ETL/ELT concepts.
- Familiarity with relational databases and data warehouse concepts.
- Experience working with API integrations and file-based data processing (CSV, Parquet, etc.).
- Hands-on experience with Git, version control workflows, pull requests, and code reviews.
- Understanding of data quality principles, including validation, reconciliation, schema checks, and error handling.
- Ability to analyze logs, troubleshoot issues, and communicate findings clearly.
- Strong analytical and problem-solving skills.
- Intermediate or higher English level.
Nice to Have
- Experience with Azure Data Factory, Azure Databricks, PySpark, Snowflake, Delta Lake, Azure Data Lake Storage, or similar cloud data technologies.
- Exposure to modern data platform architectures, including Medallion (Bronze-Silver-Gold) data processing patterns.
- Familiarity with Power BI or other BI/reporting platforms.
- Knowledge of cloud environments and DevOps practices.
- Experience with data validation frameworks such as Pandera.
- Understanding of CI/CD pipelines and deployment processes.
What Makes You a Great Fit
- You enjoy working with data and solving technical problems.
- You are detail-oriented and take ownership of the quality of your work.
- You are comfortable working within established processes while continuously learning new technologies.
- You communicate clearly and collaborate effectively with technical and non-technical stakeholders.
- You are proactive, curious, and eager to grow your data engineering skills in a production environment.