Offer summary

Qualifications:

5+ years of experience in data engineering with strong proficiency in Python and Azure Services., Expertise in Azure Data Services including Azure SQL Database, Azure Data Lake, and Azure Databricks., Solid understanding of data warehousing, ETL concepts, and DataOps principles., Familiarity with cloud-native data services and experience in performance monitoring for cloud-based solutions..

Key responsibilities:

Design and develop scalable data pipelines using Azure Data Factory and Databricks Workflows.

Optimize pipeline performance and implement data quality processes to ensure integrity.

Collaborate with stakeholders to gather requirements and troubleshoot data issues.

Support data governance initiatives and contribute to data migration projects to cloud platforms.

Job description

Job Description:

Responsibilities:

Design and develop scalable, efficient data pipelines using Azure Data Factory and Databricks Workflows.
Optimize pipeline performance for scalability, throughput, and reliability with minimal latency.
Implement robust data quality, validation, and cleansing processes to ensure data integrity.
Collaborate with stakeholders to gather business and technical requirements for data solutions.
Troubleshoot and resolve data ingestion, transformation, and orchestration issues.
Support analytics, data science, and machine learning workloads through seamless data integration.
Support data governance initiatives, ensuring compliance with data security, privacy, and quality standards.
Contribute to data migration projects including OLTP/OLAP workloads and very large datasets (VLDs) to cloud platforms (SaaS, PaaS, IaaS).

Required Skills:

5+ years of experience in data engineering, Strong proficiency in Python and familiarity with Azure Services is required.
Expertise with Azure Data Services: Azure SQL Database, Azure Data Lake, Azure Storage, Azure Databricks.
Experience with data pipeline development, orchestration, deployment, and automation using ADF, Databricks, Azure DevOps/GitHub Actions.
Proficiency in Python, Scala, and T-SQL.
Solid understanding of data warehousing and ETL concepts including star/snowflake schemas, fact/dimension modeling, and OLAP.
Familiarity with DataOps principles, Agile methodologies, and continuous delivery.
Proficient in data provisioning automation, data flow control, and platform integration.
Knowledge of both structured, semi-structured, and unstructured data ingestion, exchange, and transformation.
Experience with cloud-native data services such as DaaS (Data-as-a-Service), DBaaS (Database-as-a-Service), and DWaaS (Data Warehouse-as-a-Service), and infrastructure elements like Key Vault, VMs, and disks.
Experience with commercial and open-source data platforms, storage technologies (cloud and on-prem), and the movement of data across environments.
Experience in performance monitoring and tuning for cloud-based data solutions.
Experience supporting digital product development, data analysis, data security, and secure data exchange across platforms.
Proven experience designing enterprise-scale data architectures with high availability and security.
Understanding of data governance, data security, compliance, and metadata management.
Proficient in entity-relationship (ER) modeling and dimensional modeling.
Strong knowledge of normalization/denormalization techniques to support analytics-ready datasets.