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• No of position :- ( 1 )

• Post :- 3rd Jan 2024

• Architect, build, and maintain scalable and reliable data pipelines including robust data quality as part of data pipeline which can be consumed by analytics and BI layer.

•Design, develop and implement low-latency, high-availability, and performant data applications and recommend & implement innovative engineering solutions.

• Design, develop, test and debug code in Python, SQL, PySpark, bash scripting.

•Design and implement data quality framework and apply it to critical data pipelines to make the data layer robust and trustworthy for downstream consumers.

•Design and develop orchestration layer for data pipelines which are written in SQL, Python and PySpark.

•Apply and provide guidance on software engineering techniques like design patterns, code refactoring, framework design, code reusability, code versioning, performance optimization, and continuous build and Integration (CI/CD) to make the data analytics team robust and efficient.

Requirements:

•5+ years of experience with Bachelors / master’s degree in computer science, Engineering, Applied mathematics or related field.

•Extensive hands-on development experience in Python, SQL and Bash.

• Extensive Experience in performance optimization of data pipelines.

• Extensive hands-on experience working with cloud data warehouse and data lake platforms like Databricks, Redshift or Snowflake.

• Familiarity with building and deploying scalable data pipelines to develop and deploy Data Solutions using Python, SQL, PySpark.

•Extensive experience in developing end-to-end orchestration layer for data pipelines using frameworks like Apache Airflow, Prefect, Databricks Workflow.

•Familiar with :

o RESTful Webservices (REST APIs) to be able to integrate with other services.

o API Gateways like APIGEE to secure webservice endpoints.

o Data pipelines, Concurrency and parallelism.

• Experience in creating and configuring continuous integration/continuous deployment using pipelines to build and deploy applications in various environments and use best practices for DevOps to migrate code to Production environment.

•Ability to investigate and repair application defects regardless of component: front-end, business logic, middleware, or database to improve code quality, consistency, delays and identify any bottlenecks or gaps in the implementation.

• Ability to write unit tests in python using unit test library like pytest.

• Experience in using and implementing data observability platforms like Monte Carlo Data, Metaplane, Soda, bigeye or any other similar products.

•Expertise in debugging issues in Cloud environment by monitoring logs on the VM or using AWS features like Cloudwatch.

• Experience with DevOps tech stack like Jenkins and Terraform.

• Experience working with concept of Observability in software world and experience with tools like Splunk, Zenoss, Datadog or similar.

• Experience in developing and implementing Data Quality framework either home grown or using any open-source frameworks like Great Expectations, Soda, Deequ.