Junior Data Engineer

• Proficiency in SQL and data querying fundamentals.


• Understanding of relational databases and structured data management.


• Knowledge of data pipelines, ETL/ELT processes, and batch data processing concepts.


• Ability to troubleshoot, debug, and resolve technical issues.


• Familiarity with version control systems, particularly Git.


• Exposure to Python or similar scripting languages for data-related tasks.


• Understanding of data warehousing, dimensional modelling, and analytical data concepts.


• Familiarity with workflow orchestration tools such as Airflow or SQL Server Agent.


• Knowledge of cloud and on-premises storage concepts.


• Awareness of distributed processing, streaming architectures, and lakehouse technologies.


• Exposure to modern data engineering tools and platforms, including Spark, PySpark, Kafka, Redpanda, ClickHouse, Iceberg, and Delta Lake.


• Understanding of monitoring, observability, alerting, and operational support practices for data workloads.


• Familiarity with CI/CD pipelines, deployment automation, and engineering best practices.


• Ability to work effectively with technical documentation, runbooks, and team standards.


• Strong analytical, problem-solving, and communication skills.


• Ability to collaborate with technical and non-technical stakeholders.


• High attention to detail when working with business-critical data.


• Strong accountability, ownership mindset, and commitment to continuous learning and professional growth in data engineering.

Job Responsibilities:

Data Pipeline Support & Development

• Help build, maintain, and support batch data pipelines under the guidance of more experienced engineers.


• Assist with ingestion, transformation, and data preparation tasks across the enterprise data platform.


• Write and maintain SQL queries, scripts, and basic processing logic for data workflows.


• Support the improvement of existing pipelines by helping resolve defects, inefficiencies, and data issues.

Data Platform Operations

• Monitor scheduled pipelines and help investigate failures, alerts, and data discrepancies.


• Assist in validating data loads and checking that datasets are complete, accurate, and available when expected.


• Help maintain documentation for data flows, processes, dependencies, and operational procedures.


• Contribute to testing, deployment, and support activities for data-related changes.

Learning & Platform Growth

• Learn data engineering standards, patterns, and tools used across the team.


• Work with senior and intermediate engineers to understand platform architecture, pipeline design, and production support practices.


• Contribute to continuous improvement initiatives through automation, clean-up work, and better documentation.


• Build technical capability over time in areas such as orchestration, data quality, lakehouse concepts, streaming, and scalable data processing.

Collaboration & Delivery

• Work with BI, analytics, software engineering, and business teams to understand data requirements and support delivery outcomes.


• Participate in team planning, estimation, development, testing, and release activities.


• Ask for guidance when needed and apply feedback constructively.


• Contribute to a collaborative engineering culture through clear communication and reliable follow-through.

Tech Environment:

The platform may include a combination of established and modern technologies such as, SQL Server, Python, Airflow, Azure DevOps / Git, Spark or PySpark, Object storage, Iceberg, Kafka, or  Redpanda, ClickHouse or similar columnar analytical stores and CI/CD tooling and engineering workflows.

Job Specification:

• Degree, diploma, or relevant certification in IT, Computer Science, Engineering, Information Systems, or a related technical discipline.


• Minimum 1+ years proven experience in data engineering, software development, ETL/ELT, database development, or a related technical role.


• Foundational SQL skills, including writing queries, joining datasets, and working with basic transformations.


• Some exposure to Python, data processing, scripting, or automation.


• Understanding of relational databases and structured data concepts.


• Willingness to learn modern data platform tools, engineering practices, and production support processes.

Technical Skills

You have a basic working understanding of:

• SQL and data querying fundamentals.


• Relational databases and structured data handling.


• Data pipelines, ETL/ELT, or batch-processing concepts.


• Debugging and troubleshooting technical issues.


• Version control concepts such as Git.

Exposure to the following would be advantageous:

• Python or similar scripting languages.


• Data warehousing, dimensional modelling, or analytical data concepts.


• Workflow orchestration tools such as Airflow or SQL Server Agent.


• Cloud or on-premises object storage concepts.


• Distributed processing, streaming, or lakehouse concepts.


• Tools such as Spark, PySpark, Kafka, Redpanda, ClickHouse, Iceberg, or Delta Lake.

Platform & Engineering Practices:

• Interest in learning monitoring, alerting, observability, and operational support for data workloads.


• Exposure to CI/CD, deployment automation, or engineering workflows is beneficial.


• Comfort working with documentation, runbooks, and team standards.

Personal Attributes:

• Positive learning mindset and willingness to ask questions.


• Strong sense of accountability and follow-through.


• Good problem-solving and communication skills.


• Comfortable collaborating with both technical and non-technical stakeholders.


• Attention to detail and willingness to work carefully with business-critical data.


• Motivation to build a long-term career in data engineering.

Living the Spirit 

• Engages in cross-functional collaboration and problem solving while contributing to an inclusive team culture.


• Supports a culture of adaptability and shared accountability across the department and wider business.


• Shows up authentically and contributes to team success by working effectively with diverse colleagues and perspectives.


• Approaches challenges as opportunities to learn, improve, and help others grow.