Data Engineers

Blue Pearl

Johannesburg

On-site

ZAR 900,000 - 1,500,000

Full time

7 days ago

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Job summary

Blue Pearl in Johannesburg seeks an experienced Data Engineer to design scalable data platforms and modernise cloud-native data ecosystems for enterprise clients. You will lead end-to-end data pipelines using Python/SQL and tools like Azure Data Factory, AWS Glue, Google Dataflow, Databricks and dbt.

The role requires strong cloud experience across Azure, AWS or GCP, with a focus on CI/CD, data governance and stakeholder collaboration to deliver robust analytics and insights.

Qualifications

3–5 years hands-on data engineering experience for intermediate level.
Strong Python/SQL skills with query optimisation.
Experience with relational and non-relational databases; data pipelines and models.

Responsibilities

Design and build scalable data platforms using cloud-native and lakehouse architectures.
Develop data pipelines with Python, SQL and tools like ADF, Glue, Dataflow, Databricks and dbt.
Modernise on-prem environments to cloud-native platforms (Fabric, Synapse, Redshift, BigQuery, Databricks).
Engage with clients to conceptualise data solutions aligned to business strategy.
Support pre-sales activities, PoCs, and technical proposals.
Provide tech guidance and mentorship to junior/intermediate consultants.
Lead technical reviews and contribute to growth plans.
Identify opportunities to automate processes and improve scalability.
Collaborate with executives, product and analytics teams on data needs.
Drive knowledge sharing via blogs, internal forums and workshops.
Balance billable work with team support responsibilities.

Skills

Python

PySpark

SQL

dbt

Git

Education

Bachelor's degree in Computer Science / Information Systems / Information Technology or related field

Master's degree is advantageous

Tools

Azure Data Factory

AWS Glue

Google Dataflow

Databricks

dbt

Job description

Key Responsibility

Design and build scalable data platforms using modern cloud-native and Lakehouse architectures
Develop and optimise data pipelines using Python, SQL, and tools such as Azure Data Factory, AWS Glue, Google Cloud Dataflow, Databricks, and dbt
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
Engage with clients to conceptualize data solutions aligned to their business strategy
Support our sales team with pre-sales activities, proof-of-concept deliveries, and technical proposals
Provide technical guidance and mentorship to junior and intermediate consultants
Lead technical reviews and contribute to consultants' growth plans
Identify opportunities to automate manual processes, optimise data delivery, and improve infrastructure scalability
Work with stakeholders, including executive, product, and analytics teams, to address data infrastructure needs
Drive knowledge sharing through technical blogs, internal forums, and workshops
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.

Certifications

One or more of the following certifications would be advantageous: