Sr. Data Architect (Azure) || US Healthcare domain

EMIDS

[HIDDEN TEXT]

Exp :

Location

Skills : Data Engineering, Azure, ETL, Pyspark, Databricks,

Job Summary:

We are seeking an experienced and highly skilled Senior Data Architect to lead the design, implementation, and optimization of our modern data architecture. The ideal candidate will bring deep technical expertise in data engineering, cloud platforms (preferably Azure), and big data tools such as Databricks and PySpark, along with a strong foundation in ETL design and development. You will play a key role in transforming our data strategy, driving best practices, and mentoring engineering teams.

Key Responsibilities:

• Design and architect scalable, secure, and high-performance data solutions in Azure cloud environments.
• Lead the development and optimization of ETL/ELT pipelines using Azure Data Factory, Databricks, PySpark, and other tools.
• Define and enforce data architecture standards, frameworks, and best practices.
• Collaborate with data engineers, data scientists, business stakeholders, and product teams to understand requirements and deliver reliable data solutions.
• Provide hands-on guidance and mentorship to junior and mid-level data engineers.
• Ensure data quality, governance, security, and compliance in all architectural solutions.
• Evaluate and recommend tools, technologies, and processes to improve data platform efficiency and scalability.
• Lead performance tuning, data modeling, and real-time data integration efforts.

Required Skills and Qualifications:

• 12+ years of experience in data architecture, data engineering, and enterprise data solutions.
• Strong expertise in Azure Data Services (Azure Data Lake, Azure Data Factory, Azure Synapse, etc.).
• Hands-on experience with Databricks and PySpark for large-scale data processing.
• Advanced knowledge of ETL/ELT pipelines design, development, and orchestration.
• Experience with structured and unstructured data processing, including batch and streaming data.
• Solid understanding of data modeling, data warehousing, and cloud-native architectures.
• Proficiency in SQL, Python, and Spark for data transformation and analysis.
• Familiarity with CI/CD pipelines, version control (e.g., Git), and agile methodologies.
• Excellent communication and leadership skills, with the ability to influence cross-functional teams.