Sr.Data Engineering

Role & responsibilities

Job Description: Azure Sr. Data Engineer

Overall Exp: Up-to 10 yrs

6-7 years of relevant experience.

Should be flexible to adapt to project requirements needs.

Key Responsibilities:

• Data Pipeline Development: Design, build, and optimize robust, scalable, and efficient ETL/ELT data pipelines using Python and PySpark, primarily within Azure Databricks and Azure Data Factory.
• Data Ingestion & Processing: Develop and manage processes for ingesting data from various sources (e.g., transactional databases, APIs, batch and streaming sources) and transform it into clean, usable formats for downstream consumption.
• Streaming Data Extractions using Kafka, Azure Event Hubs
• Data Quality & Monitoring: Implement comprehensive unit and integration test coverage for data pipelines. Establish and maintain monitoring, alerting, and dashboarding solutions (e.g., Grafana) for data quality, pipeline health, and performance.
• Cloud Infrastructure Management (OpenShift/Azure): Contribute to the setup, configuration, and maintenance of data-related infrastructure on OpenShift, ensuring deployment readiness and leveraging tools like HELM for application packaging and deployment.
• CI/CD & Automation: Drive CI/CD best practices using GitHub Actions, ensuring automated testing (unit tests), build, and deployment processes for data solutions to environments like OpenShift.
• SQL & Data Modeling: Develop and optimize complex SQL queries for data extraction, transformation, and loading. Apply strong data modeling principles for efficient data storage and retrieval in SQL Server and other data stores.
• Azure Ecosystem Leverage: Utilize a broad range of Azure data and analytics services, including Azure Data Factory, Azure Databricks, Azure SQL Server, Azure Key Vault, Azure Functions, and others to build comprehensive data solutions.
• Performance Optimization: Proactively identify and resolve performance bottlenecks in data pipelines and databases through query optimization, indexing strategies, and efficient data processing techniques.
• Collaboration & Documentation: Work closely with data scientists, analysts, and other engineering teams to understand data requirements. Create clear and concise documentation for data pipelines, architecture, and processes.

Required Core Skills & Qualifications:

• Programming & Data Processing: Strong proficiency in Python and PySpark for large-scale data processing and ETL development.
• Experience with Pipeline Orchestration tools such as Airflow.
• Data Warehousing & SQL: Expertise in SQL for complex querying, data manipulation, and schema design. Proven experience in SQL optimization and performance tuning.
• ETL Development: Demonstrable experience in designing, building, and maintaining robust ETL/ELT data pipelines.
• Cloud Data Platform (Azure Focus): Hands-on experience with Azure Databricks. Proficiency with core Azure Analytics Services including Azure Data Factory, Azure SQL Server, and Azure Key Vault.
• DevOps & CI/CD: Experience implementing CI/CD pipelines from GitHub (including GitHub Actions) for automated testing (unit tests), build, and deployment processes.
• Containerization & Orchestration: Familiarity and practical experience with OpenShift (setup, deployment-ready configurations, and management). Experience with HELM for deploying applications on Kubernetes/OpenShift.
• Monitoring & Observability: Experience in setting up and configuring Grafana for dashboards to monitor data quality and pipeline health.

Preferred Qualifications:

• Bachelor's / masters degree in computer science, Engineering, Data Science, or a related quantitative field. (B. Tech / M.C.A)
• Relevant Azure certifications (e.g., Azure / Databricks Certified Data Engineer Associate).
• Experience with real-time data processing frameworks (e.g., Kafka, Azure Event Hubs).
• Understanding data governance, data security, and compliance best practices.