Senior Data Engineer / Analytics Engineer

Role Description

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.

Duties and Responsibilities

• 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

Qualifications

• 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

Nice to Have

• 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)

Minimum Requirements

• 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

What We're Not Looking For

• 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