高级数据工程师 / 分析工程师（AWS）
Senior Data Engineer / Analytics Engineer (AWS)
DVT 是大陆领先的软件开发公司之一。我们的工程师在南非及全球的领先公司中参与最前沿平台的咨询工作。你将与国内最资深的专业人士一起工作，使用现代数据栈中的最新技术。
我们以持续学习的文化、内部知识分享以及 AWS 和数据生态系统中的赞助技术活动而自豪。
我们正在寻找一位高级数据工程师 / 分析工程师加入我们的数据与自动化业务，在高影响力客户项目中工作。你将帮助设计、构建和运营一个以 AWS 为主的现代数据平台——将数据从 S3 移动到 Redshift Serverless，由 Airflow 协调，使用 dbt 进行建模，用 Python 编写脚本，未来可能向 Snowflake 迁移。
这是一个面向客户的远程工作角色。你将全程负责数据管道，塑造分析工程实践，并与分布式利益相关者清晰沟通。这不是一个通用的后端工程岗位。强大的软件工程师只有在具备现代云数据平台的实际经验时才具有价值。
职责和要求
数据平台与管道
- 在 AWS 原生数据环境中设计、构建和维护稳健的 ETL/ELT 管道
- 负责 Airflow 协调——调度、依赖关系、重试、警报和运维支持
- 使用模块化、仓库优先的转换模式，在 dbt 中开发可分析的数据模型
- 在 S3（原始、暂存、精炼）和 Redshift Serverless 中自信地进行存储和仓储操作
- 参与路线图制定，并可能参与未来向 Snowflake 的迁移
工程与质量
- 编写干净、可维护的 Python 代码用于管道逻辑、脚本和轻量级工程任务
- 将数据质量、测试和可观测性嵌入每条管道——而不是事后补充
- 对数据工作负载应用良好的版本控制、代码审查和 CI/CD 实践
客户与协作
- 直接与客户利益相关者互动：收集需求、展示解决方案并提供建议
- 在分布式、远程优先的环境中与分析师、产品团队和其他工程师合作
- 参与架构评审、回顾会议，并持续改进平台实践
所需经验和技能
- 5 年以上数据工程、分析工程或相关职位的经验
查看英文原文
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 / AnalyticsEngineer 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.
Requirements
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
REQUIRED EXPERIENCE AND SKILLS
- 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
Highlights
Senior Data Engineer | Fully Remote | 2-yr contract. Modern AWS data stack — Airflow, dbt, Python, S3, Redshift, Snowflake. Join DVT on a high-impact fintech build.
Originally posted on Himalayas