Posted today · be early
Senior Data Engineer / Analytics Engineer (AWS)
DVT
WorldwideremotePosted today
Skill Required
Data-EngineeringAnalytics-EngineeringAWS-Data-PlatformData-PipelinesETL-ELT-EngineeringSenior-Data-Analytics-EngineerSenior-Data-EngineeringSenior-Cloud-Data-EngineerAnalytics-EngineerData-EngineerData EngineerAnalytics EngineeranalyticsAWSdata engineeringtroubleshootingAirflowData Warehousingrelated fieldObservabilityEngineeringServerlessautomationSnowflakedesigningRedshiftDatadogfintechwrittenTestNGKafkaPythondesignCI/CDCloudDesign PatternsdbtContract
Key highlights
- Experience: 5+ years
- Remote: Fully Remote
- Duration: 2-yr contract
- Stack: AWS, Airflow, dbt, Python, S3, Redshift, Snowflake
- Client: High-impact fintech build
Role overview
DVT is one of the top software development companies on the continent, with engineers consulting on cutting-edge platforms at leading companies across South Africa and globally. They are looking for a Senior Data Engineer / Analytics Engineer to join their Data and Automation practice on a high-impact client engagement to 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 where the successful candidate will own pipelines end to end, shape analytics engineering practices, and communicate clearly with distributed stakeholders.
Responsibilities
- 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
- 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
- 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
Requirements
- 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
- Matric (Grade 12) certificate
- Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field (or equivalent practical experience)
- Reliable home-office setup and connectivity suitable for a fully remote client engagement
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)
- AWS certification advantageous (e.g. Data Engineer – Associate, Solutions Architect – Associate/Professional)
Benefits
- Culture of continuous learning, internal knowledge sharing, and sponsored technical events across the AWS and data ecosystem
Additional details
- 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.
- WHAT WE'RE NOT LOOKING FOR: Pure backend / application-only engineers with no production data platform work
- WHAT WE'RE NOT LOOKING FOR: Candidates with no real orchestration experience
- WHAT WE'RE NOT LOOKING FOR: Candidates with no warehouse or data modelling background
- WHAT WE'RE NOT LOOKING FOR: Profiles without AWS exposure
- WHAT WE'RE NOT LOOKING FOR: Candidates who cannot clearly articulate the data work they've shipped
- Originally posted on Himalayas
