Data Engineer | DISA-DIA-DE-JHB-0058

Decision Inc. is seeking talented Data Engineers to join our Data, Information, and Analytics practice as client-facing consultants. You will design and build modern data platforms, lead delivery across diverse industries, and help our clients become genuinely data-driven businesses.

You will work across the full engineering lifecycle, from architecture and pipeline development to mentoring junior colleagues and contributing to pre-sales engagements. A passion for data, a curiosity for new technologies, and the ability to adapt quickly to different business environments are essential.

Who Are We?

Decision Inc. is a global digital partner that enables businesses to reinvent themselves to realise their full potential.

We deliver agility, resilience, and intelligence to any enterprise, enabling them to adapt quickly and thrive through innovation and opportunity. Our teams have engaged with more than 400 clients globally over the past 15 years, providing them with the expertise to build, operate, and optimise their digital businesses.

We achieve this by leveraging the world's best technology to drive our client's business ambitions forward into tomorrow's reality. These technologies include but are not limited to: Microsoft, SAP, Qlik, Snowflake, Alteryx, Automation Anywhere, and Workday Adaptive Insights.

Our success is ultimately driven by our entrepreneurial culture, industry exposure, and the passion of our people. We have three key cross-functional teams – Functional and Specialist Consulting, Development, and Engineering teams. Based in four global offices, our exceptional consultants focus on delivering value to clients by keeping them at the centre of our service delivery. To find out more about our exciting fast-paced culture visit: Careers at Decision Inc.

What Will You Do?

Key Responsibility:

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

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 conceptualise 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

Who Do You Need to Be?

Intermediate (3–5 years' experience)

3–5 years of hands-on experience in data engineering

Strong proficiency in Python and/or SQL, including query optimisation and working with both relational and non-relational databases

Experience designing and building data pipelines, data models, and lakehouse architectures (medallion pattern)

Practical experience with one or more cloud platforms: Microsoft Azure (Data Factory, ADLS Gen2, Synapse Analytics, Fabric), AWS (Glue, S3, Redshift, EMR), or GCP (Dataflow, BigQuery, Cloud Storage, Dataproc)

Familiarity with Databricks, Snowflake, Delta Lake, and PySpark

Understanding of data transformation frameworks such as dbt

Experience with version control (Git) and CI/CD practices for data workflows

Strong analytical skills and the ability to perform root cause analysis on complex data problems

Good communication and stakeholder engagement skills

Senior (6–8+ years' experience)

6–8+ years of hands-on experience in data engineering

All of the above, plus demonstrable experience leading end-to-end platform delivery

Experience architecting enterprise-grade lakehouses and data mesh patterns

Proficiency with infrastructure-as-code tools (Terraform, Bicep, AWS CDK, or Pulumi) and DevOps pipelines

Proven ability to work with cross-functional teams in a dynamic consulting environment

Track record of mentoring junior engineers and contributing to technical strategy

Qualifications

Bachelor's degree in computer science, Information Systems, Information Technology, or a related field

A master's degree in a relevant field is advantageous

Certifications

Primary certifications – one or more:

DP-700

Microsoft Fabric Data Engineer Associate

DP-203

Microsoft Azure Data Engineer Associate

Databricks DE Associate

Databricks Certified Data Engineer Associate

Google Professional

Professional Data Engineer (GCP)

AWS DEA-C01

AWS Certified Data Engineer – Associate

Databricks DE Professional

Databricks Certified Data Engineer Professional

Originally posted on Himalayas