About Smart Working
At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being.
Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.
Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.
About the Role
As a Senior Python Engineer (Backend / Data), you will join an existing engineering team to strengthen backend development and data engineering capabilities. This is a long-term, ongoing opportunity (subject to confirmation), focused on building and maintaining reliable production systems.
You will work closely with the engineering team and an existing DevOps/SRE engineer on infrastructure, deployments and releases. The role requires independent ownership of assigned work, collaboration across engineering functions and an understanding of how data pipelines support the wider product.
You will contribute to engineering decisions, maintain high standards of data quality, correctness and testing, and propose solutions rather than simply execute assigned tasks. Reliability and thoroughness in production are key priorities.
Design, build and maintain reliable data pipelines in Python on AWS.
Build and maintain backend services and APIs using Django and FastAPI.
Model, query and optimise data across PostgreSQL and search/NoSQL databases, including OpenSearch, Elasticsearch and DynamoDB.
Deploy and run services on Kubernetes, working alongside the DevOps engineer.
Take end-to-end ownership of assigned work, including design, testing, code reviews, releases and production support.
Work independently on assigned tasks while collaborating closely with the engineering team and DevOps/SRE engineer on infrastructure and releases.
Maintain high standards of data quality, correctness and testing while understanding how data pipelines contribute to the wider product.
Follow established engineering practices, including code reviews and CI/CD, while adapting to changing priorities.
Proactively identify problems, propose solutions and challenge technical decisions when necessary rather than simply executing tickets.
Prioritise thoroughness, reliability and production stability when developing and maintaining data pipelines and data stores.
6+ years of experience in software engineering, including 5+ years of commercial Python development.
5+ years of Python backend development experience, with strong hands-on engineering capabilities.
3+ years of experience in data pipelines and data engineering, including ETL/ELT processes and batch processing. Relevant technologies include Airflow, Dagster, Prefect, AWS Glue and Step Functions.
3+ years of AWS experience, with practical experience developing and maintaining cloud-based backend services and data pipelines. Strong GCP or Azure experience may be considered on a case-by-case basis.
3+ years of experience with Python web frameworks, particularly Django or FastAPI. Flask is an acceptable alternative.
3+ years of PostgreSQL experience, including data modelling, querying and optimisation. MySQL or SQL Server experience may be considered where strong SQL expertise is demonstrated elsewhere.
2+ years of experience with non-relational or search databases, such as Elasticsearch, OpenSearch or DynamoDB. Relevant alternatives include MongoDB, Redis and Cassandra.
2+ years of Kubernetes experience, including deploying and running services. Docker combined with ECS or EKS exposure may be considered as an alternative.
Ability to independently own technical tasks from design through implementation, testing, release and production support.
Strong attention to data quality, correctness, testing and production reliability.
Ability to collaborate with engineering and infrastructure teams while independently proposing solutions and challenging decisions where appropriate.
Ability to follow engineering practices and adapt to changing priorities.
1+ year of experience with workflow orchestration, using Airflow or similar technologies. Alternatives include Celery and cron-based schedulers.
1+ year of experience with streaming or event-processing technologies, such as Kafka, Kinesis or SQS. RabbitMQ is an acceptable alternative.
1+ year of experience with Infrastructure as Code and CI/CD, including Terraform and GitHub Actions. Alternatives include Pulumi and GitLab CI.
Previous experience leading a technical project or taking complete end-to-end ownership of a data pipeline or backend service
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Smart Working Solutions focuses on developing and implementing advanced Voice AI and conversational AI solutions. They specialize in agent configuration, optimization, and integration with business systems.