Data Engineer

Blue Pearl

Johannesburg, South Africa

ZAR 600,000 – ZAR 900,000

Key Responsibility:

You will be assigned a portfolio of client engagements where you will be expected to:

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Design and build scalable data platforms using modern cloud-native and Lakehouse architectures

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Develop and optimise data pipelines using Python, SQL, and tools such as Azure Data Factory, AWS Glue, Google Cloud Dataflow, Databricks, and dbt

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Modernise legacy data environments, migrating from on-premises solutions to cloud-native platforms such as Microsoft Fabric, Azure Synapse Analytics, AWS Redshift, Google BigQuery, or Databricks

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Engage with clients to conceptualize data solutions aligned to their business strategy

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Support our sales team with pre-sales activities, proof-of-concept deliveries, and technical proposals

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Provide technical guidance and mentorship to junior and intermediate consultants

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Lead technical reviews and contribute to consultants' growth plans

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Identify opportunities to automate manual processes, optimise data delivery, and improve infrastructure scalability

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Work with stakeholders, including executive, product, and analytics teams, to address data infrastructure needs

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Drive knowledge sharing through technical blogs, internal forums, and workshops

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Balance billable project work with team support responsibilities

Requirements

Data Engineer – Candidate Requirements

Intermediate Level

3–5 years' experience

3–5 years of hands-on experience in data engineering.
Strong proficiency in Python and/or SQL , including query optimisation.
Experience working with both relational and non-relational databases.
Experience designing and building data pipelines and data models.
Understanding and practical experience with lakehouse architectures , including the medallion pattern.
Practical experience with at least one major cloud platform, including:
- Microsoft Azure
- AWS
- Google Cloud Platform (GCP)
Familiarity with:
- Databricks
- Snowflake
- Delta Lake
- PySpark
Understanding of data transformation frameworks such as dbt .
Experience with version control using Git .
Understanding of CI/CD practices for data workflows.
Strong analytical and problem-solving skills.
Ability to perform root-cause analysis on complex data issues.
Good communication and stakeholder engagement skills.

Senior Level

6–8+ years' experience

6–8+ years of hands-on experience in data engineering.
All intermediate-level technical requirements, together with demonstrable experience in:
- Leading end-to-end data platform delivery.
- Architecting enterprise-grade lakehouse environments.
- Implementing data mesh patterns.
- Infrastructure-as-code using tools such as Terraform, Bicep, AWS CDK or Pulumi.
- DevOps and CI/CD pipelines.
- Working effectively with cross-functional teams in a dynamic consulting environment.
- Mentoring junior engineers.
- Contributing to technical strategy and solution direction.

Qualifications

Bachelor's degree in:
- Computer Science
- Information Systems
- Information Technology
- or a related field.
Master's degree in a relevant field is advantageous.