Junior Data Engineer

BET Software

Date: 4 days ago
City: Johannesburg, Gauteng
Contract type: Full time
  • 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.

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