DVT is one of the top software development companies on the continent. Our engineers consult on cutting-edge platforms at leading companies across South Africa and globally. You'll work alongside some of the most established practitioners in the country, on the latest technologies in the modern data stack.
We are proud of our culture of continuous learning, internal knowledge sharing, and sponsored technical events across the AWS and data ecosystem.
We are looking for a Senior Data Engineer / Analytics Engineer to join our Data and Automation practice on a high-impact client engagement. You will help design, build, and operate a modern AWS-first data platform — moving data through S3 into Redshift Serverless, orchestrated by Airflow, modelled with dbt, and scripted in Python, with a likely evolution towards Snowflake.
This is a client-facing role in a fully remote environment. You will own pipelines end to end, shape analytics engineering practices, and communicate clearly with distributed stakeholders.This is not a generic backend engineering role. Strong software engineers are only relevant where they bring credible, hands-on experience in a modern cloud data platform.
Data Platform & Pipelines
Design, build, and maintain robust ETL/ELT pipelines across AWS-native data environments
Own Airflow orchestration — scheduling, dependencies, retries, alerting, and operational support
Develop analytics-ready data models in dbt, using modular, warehouse-first transformation patterns
Work confidently across S3 (raw, staged, curated) and Redshift Serverless for storage and warehousing
Contribute to the roadmap and potential migration toward Snowflake as a future warehouse
Engineering & Quality
Write clean, maintainable Python for pipeline logic, scripting, and lightweight engineering tasks
Embed data quality, testing, and observability into every pipeline — not as an afterthought
Apply sound version control, code review, and CI/CD practices to data workloads
Client & Collaboration
Engage directly with client stakeholders: gather requirements, present solutions, and advise on trade-offs
Partner with analysts, product teams, and other engineers in a distributed, remote-first setup
Contribute to architectural reviews, retrospectives, and continuous improvement of platform practices
5+ years in data engineering, analytics engineering, or closely related roles
Strong hands-on AWS data platform experience — S3-centred flows, cloud-native data workflows, warehouse-driven delivery
Apache Airflow — proven experience designing, maintaining, and troubleshooting production pipelines
dbt — solid analytics engineering patterns, modular models, testing, and documentation
Warehouse experience — Redshift preferred; Snowflake highly desirable; comparable warehouse backgrounds considered if adaptable
Python — confident scripting for pipelines, transformations, and automation
Strong understanding of data modelling (dimensional, wide tables, incremental strategies)
Excellent written and verbal communication — able to explain technical work credibly to non-technical audiences
Self-directed delivery in a fully remote, client-facing environment
Snowflake migration or implementation experience
Pipeline monitoring and observability (e.g. Datadog, Monte Carlo, CloudWatch, OpenLineage)
Experience implementing data quality frameworks (e.g. dbt tests, Great Expectations)
Background moving organisations from traditional warehouse-centric patterns toward modern analytics engineering
Experience in fintech, lending, or financial services environments
Exposure to event-driven or streaming patterns (Kinesis, Kafka)
Matric (Grade 12) certificate
Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field (or equivalent practical experience)
AWS certification advantageous (e.g. Data Engineer – Associate, Solutions Architect – Associate/Professional)
Reliable home-office setup and connectivity suitable for a fully remote client engagement
Pure backend / application-only engineers with no production data platform work
Candidates with no real orchestration experience
Candidates with no warehouse or data modelling background
Profiles without AWS exposure
Candidates who cannot clearly articulate the data work they've shipped
Job details are sourced from the employer's original posting.
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