Technocrats with Python experience

Job Description

About Python architect At Cybage: Providing Architectural Services for turn-key projects, new clients with large scale requirements. R&D on new technologies / frameworks / tools in Python ecosystem. Online travels on need basis for new customer engagement and during discovery phase to transfer knowledge. Python Architect - Web/API/Application Designing, building and maintaining, scalable and secure services and REST APIs, in at least one Python framework such as Django, Flask, FastAPI etc. or in Python with gRPC. Expertise in at least one one RDBMS such as Postgres, MySQL, Oracle etc. and one NoSQL database such as MongoDB, Redis, etc. Familiarity with different caching strategies and use of at least one caching solution such as Redis, Memcached, etc. to do the same. Designing for distributed / asynchronous jobs and familiarity with tools such as Celery, Redis Queue, Kafka, etc. to implement the same. Building these services in at least one cloud platform such as AWS, Azure, GCP etc. with an eye on scalability, performance and high availability. Experienced in building these use containerization ( Docker, etc. ) and orchestration ( Kubernetes, etc. ) techniques. Ensuring code quality and use of at least one tool / linter such as pylint, black, flake8 etc. Using automated tests to validate the services and APIs built in order to allow for continuous delivery. Effectively monitor and observe these services by tracking application logs through at least one logging tool such as Splunk, Datadog Logs, AWS Cloudwatch, etc. and application metrics such as latency, concurrency, etc. over at least one monitoring tool such as Datadog APM/RUM, AWS Cloudwatch, etc. Expertise in identifying the right thresholds and alerts on these services and plugging these against alerting tools such as PagerDuty, OpsGenie, AWS Cloudwatch alarms etc. in order to respond to incidents quickly and effectively. Python Architect - Data Designing, building and maintaining, effective and scalable data solutions using Python. Creating and maintaining data integration processes, ETL ( Extract, Transform, Load ) workflows, and data pipelines ( Airflow, etc. ) to seamlessly transport data between systems. Expertise in parallel processing massive datasets and use of Spark, Hadoop, etc. to do the same. Experienced in working with datasets hosted in at least one data warehouse such as Snowflake, Amazon Redshift, etc. Familiarity with reporting on datatsets using at least one BI tool such as Looker, Tableau, Power BI, Quicksight etc. Expertise in at least one one RDBMS such as Postgres, MySQL, Oracle etc. and one NoSQL database such as MongoDB, Redis, etc. Building these in at least one cloud platform such as AWS, Azure, GCP etc. with an eye on scalability, performance and high availability. Experienced in building these use containerization ( Docker, etc. ) and orchestration ( Kubernetes, etc. ) techniques. Ensuring code quality and use of at least one tool / linter such as pylint, black, flake8 etc. Using automated tests to validate the services built in order to allow for continuous delivery. Effectively monitor and observe these services by tracking service logs through at least one logging tool such as Splunk, Datadog Logs, AWS Cloudwatch, etc. and service metrics such as latency, concurrency, etc. over at least one monitoring tool such as Datadog APM, AWS Cloudwatch, etc. Expertise in identifying the right thresholds and alerts on these services and plugging these against alerting tools such as PagerDuty, OpsGenie, AWS Cloudwatch alarms etc. in order to respond to incidents quickly and effectively. Passion for maintaining software configurations in code and familiarity in the use of at least one of Ansible, Terraform, Helm, etc. to do the same. Show more Show less