Automation Developer / Data Engineer

Job Profile: Automation/DevOps / Data Engineer

Desired Experience: 5 – 8 years of experience
Core Skills:

· Should have in in-depth knowledge and hands-on experience on Power Shell/Python/Shell scripting and automation.
· Work with Docker and Kubernetes to containerize applications and orchestrate them on GCP Kubernetes Engine (GKE) clusters for scalability and resilience.
· Implement monitoring solutions such as Google Cloud Monitoring and logging using Stackdriver or similar tools to ensure high availability and performance of GCP resources.
· Write scripts (e.g., Python(Fast API/Flask), Shell) for automating repetitive tasks, and develop custom API solutions to optimize GCP resource provisioning and management
· Design, build, and maintain continuous integration and continuous deployment (CI/CD) pipelines using tools like Jenkins, GitHub action CI/CD, or Google Cloud Build to automate software deployment.
· Experience in integration of third-party tools that expose SOAP/REST web services. Development of customized workflows for the creation of various operations for integration
· Extensive experience in defining the branching and the merging strategies, creating baselines and releasing the code.
· Experience in bug tracking tools like JIRA.
· Should have experience on Configuring, Automating and Deploying Ansible playbooks for configuration management to existing Infrastructure.
· Should have worked on Docker containers.
· Installation, Configuration and administration of Linux systems.
· Should have worked on Integration with ITSM tools for end to end process Automation, auto ticketing, deploying and patch management and deploying software updates with Configuration management tools.
· Datacentre management, Information Technology Infrastructure Library (ITIL).
· Experience in constructing High level design and Low level design of tool as per the requirement of the environment.
· Should be able to analyze customer requirements and provide automation solutions by mapping requirements to automation implementation.
· Manage customer satisfaction through effectively communicating and managing customer expectations.
· Will need strong technical writing skills and the ability to effectively communicate with managers and executives on technical and mission issues
· Database Management: Proficiency in managing and optimizing various database systems such as SQL, NoSQL, and NewSQL databases like MySQL, PostgreSQL, MongoDB, Cassandra, and others.
· ETL (Extract, Transform, Load): The ability to design, develop, and maintain ETL processes for moving and transforming data from source systems to data warehouses.
· Scripting: The ability to write Python scripts for data extraction, transformation, and loading processes, and automate data-related tasks
· Containerization and Orchestration: Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes for managing and scaling data engineering workflows.
· Streaming Data: Familiarity with streaming data technologies such as Apache Kafka, Apache Beam for Dataflow Jobs, Google Pub/Sub, and tools for processing real-time data.
· Data Pipelines: Building and managing data pipelines, including job scheduling and orchestration tools like Apache Airflow.
· Collaboration: Effective communication and teamwork with data scientists, analysts, and other stakeholders to understand and meet data requirements.
Soft Skill:
Ø Excellent Communication skills (written & verbal)
Ø Good Documentation Skills
Ø Good Presentation Skills
Ø Build and maintain relationships with stakeholders

Educational Qualification: Bachelor of Engineering/Bachelor of Technology/MCA (Master of Computer Applications)/Bachelor of Science/Bachelor of Communications

Mandate Skills : PowerShell, Python, Shell, YAML, API Development using FAST API, Ansible, AWX, Azure Automation, Terraform, GITHUB actions, CI/CD, Docker, Kubernetes, Google Cloud Functions, Cloud Run, GKE, Firestore, Big query, Apache Kafka, Apache Beam for Dataflow Jobs, Google Pub/Sub, SQL, NoSQL, Grafana, Prometheus, Elastic Search, MLOps Framework