Sr.Data Engineering

Job Description

Job Description

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6– 8 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, streaming sources) and transforming it into clean, usable formats for downstream consumption.

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, 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.

Required Core Skills & Qualifications:

Programming & Data Processing:Strong proficiency in Python and PySpark for large-scale data processing and ETL development.

Data Warehousing & SQL:

Expertise in SQL for complex querying, data manipulation, and schema design.(Optional, but highly preferred): 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.

Qualifications

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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.

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 or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field.Relevant Azure certifications (e.g., Azure Data Engineer Associate).Experience with real-time data processing frameworks (e.g., Kafka, Azure Event Hubs).Understanding of data governance, data security, and compliance best practices.

Additional Information

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