Senior Software Engineer in Test, Data / ETL (Portugal) at DoiT

São José da Laje, Alagoas, Brazil -

Full Time

Start Date

Immediate

Expiry Date

22 Jan, 26

Salary

0.0

Posted On

24 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Testing, ETL, Python, TypeScript, Airflow, BigQuery, Cloud Run, Firestore, CI/CD, GCP, AWS, Terraform, Microservices, Automation, Observability, SQL, NoSQL

Industry

technology;Information and Internet

Description

Senior Software Engineer in Test, Data / ETL Location Our Senior Software Engineer in Test, Data / ETL will be an integral part of our Engineering team in EMEA. This full-time remote role is open to employees based in the UK, Ireland, Estonia, the Netherlands, Sweden, and Israel, and to contractors across other EMEA locations, including Eastern Europe and Portugal. Who We Are DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state - from planning to production. Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide. The Opportunity As a Senior Software Engineer in Test, Data / ETL at DoiT International, you’ll take the lead in validating and automating the quality of our data pipelines, observability systems, and ETL workflows. You’ll design, build, and maintain frameworks that ensure data integrity, reliability, and performance across our cloud-native SaaS platform. This hands-on role blends software engineering, data testing, and automation strategy. You’ll partner with data engineers, SDETs, and developers to build robust validation processes for systems powered by BigQuery, Airflow, Cloud Run, and Firestore. You’ll be a driving force in shaping how data quality is measured and maintained across distributed, cloud-scale systems — helping us deliver reliable insights and performance at scale. Responsibilities Lead validation and observability for data pipelines built on BigQuery, Airflow, Cloud Run, and Firestore. Build and maintain automated test frameworks for data workflows, APIs, and microservices. Define and enforce quality gates for data pipelines, ensuring reliable and accurate transformations. Integrate validation and test automation into GitHub Actions CI/CD pipelines for seamless, reliable deployments. Collaborate across teams to ensure data scalability, reliability, and performance across all environments. Plan, design, and execute manual and automated tests to verify software and data quality. Mentor peers and champion best practices in data testing, automation, and observability. Create and maintain test plans, document and track defects, and collaborate closely with developers to improve processes and tools. Deliver additional test coverage within existing frameworks. Qualifications 6+ years of experience in QA, Data Testing, or SDET roles, with a proven track record of driving data quality and observability across large-scale systems. 4+ years of hands-on programming experience in Python, with working knowledge of TypeScript preferred. Extensive experience with data orchestration and cloud-based ETL frameworks (e.g., Airflow, Prefect, or Dagster) and modern data warehouses (e.g., BigQuery, Snowflake, or Redshift), including designing, validating, and optimizing scalable, automated data pipelines in GCP or similar environments. Deep understanding of databases, SQL, and NoSQL, with strong analytical skills for validating and troubleshooting high-volume datasets. Solid background in CI/CD pipelines, GCP/AWS, and Infrastructure as Code tools such as Terraform or Pulumi. Strong grasp of microservices architecture and the end-to-end SaaS development lifecycle, ensuring scalability, reliability, and performance. Excellent communication and leadership skills, with a history of mentoring peers, influencing engineering teams, and leading quality or automation initiatives. Bonus Points Functional test automation experience with Playwright and TypeScript. Familiarity with FinOps (Cloud Cost & Usage Management). Experience with performance and load testing frameworks. Knowledge of security and compliance best practices (e.g., PoLP). Experience working in remote-first, global teams. Are you a Do’er? Be your truest self. Work on your terms. Make a difference. We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally. What does being a Do’er mean? We’re all about being entrepreneurial, pursuing knowledge and having fun! Click here to learn more about our core values. Sounds too good to be true? Check out our Glassdoor Page. We thought so too, but we’re here and happy we hit that ‘apply’ button. Unlimited PTO Flexible Working Options Health Insurance Parental Leave Employee Stock Option Plan Home Office Allowance Professional Development Stipend Peer Recognition Program Many Do’ers, One Team DoiT unites as Many Do’ers, One Team, where diversity is more than a goal—it's our strength. We actively cultivate an inclusive, equitable workplace, recognizing that each unique perspective enhances our innovation. By celebrating differences, we create an environment where every individual feels valued, contributing to our collective success. #LI-Remote

How To Apply:

Incase you would like to apply to this job directly from the source, please click here

Responsibilities

Lead validation and observability for data pipelines and build automated test frameworks for data workflows. Collaborate across teams to ensure data scalability, reliability, and performance.