• US Citizen

• Green Card

• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

• W2-Permanent

• W2-Contract

• Contract to Hire

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 26th Jun 2025

Technical Mastery: Strong proficiency in Azure Cloud Platform, particularly Databricks, Synapse Analytics, Data Factory and Data Lake Storage, Azure SQL, and multi-dimensional data warehouse methodology.

ETL Proficiency: Experienced in building ETL (Extract, Transform, Load) workflows that support efficient data movement and processing in cloud environments. Familiarity with sourcing data from leading Saas providers and their API solutions such as Oracle Fusion Cloud ERP, Salesforce CRM and CSM, Workday HCM and relational databases like Oracle, MS SQL Server and Postgres.

Data Transformation Skills: Deep understanding of data transformation techniques to ensure accuracy and performance across all systems.

Cloud Infrastructure Knowledge: Proficiency with Azure cloud infrastructure management and best practices for data security, scalability, and cost efficiency.

Analytical Problem-Solving: Ability to troubleshoot data issues and proactively improve processes to enhance data reliability.

Code Optimization: Advanced skills in writing and optimizing complex Python, Java, JSON, Spark and SQL queries for better performance and throughput.

Data Modeling Expertise: Proven experience developing efficient data models that support large-scale analytics.

Reporting, Analytics and Visualization: Proficiency in Analytics and visualization tools like Power BI, Tableau and Oracle Analytics Cloud.

Data Governance: Strong knowledge of data governance practices, including data quality, lineage, data at rest and in-transit security and role-based access control protocols.

Collaborative Communication: Ability to work cross-functionally with data scientists, analysts, visualization teams and business users to deliver comprehensive insights.

LIMS Familiarity: Familiarity with Laboratory Information Management Systems (LIMS) such as Sunquest Antrim and Sysmex MOLIS is a big plus.

AI/ML Experience: Familiarity with applying machine learning models to data processes and enhancing data pipeline functionality.

Daily Responsibilities:
In this role, you'll be managing end-to-end data engineering and automation, ensuring the highest standards of data quality, efficiency, scalability and security. Your day-to-day will include:

Pipeline Management: Oversee and refine data pipelines, ensuring data is collected, transformed, and stored accurately across all systems. This includes monitoring and troubleshooting pipelines to prevent data loss, downtime and improve efficiency.

ETL Development: Build, maintain, and improve ETL workflows that support data integration from multiple sources, handling various data types and frequencies. This includes event-driven solutions to sequence and automate Azure-centric data pipelines.

Data Modeling & Transformation: Collaborate with a Data Management team to design data models that align with business needs and perform data transformations to create usable data sets for analysis and reporting.

SQL Query Optimization: Write, test, and optimize SQL queries to ensure efficient data retrieval and processing.

Collaboration with Analytics Teams: Work closely with data scientists, analysts, and stakeholders to support data requests, visualization needs, and other analytics functions.

Cloud Infrastructure Management: Monitor and manage an Azure Data Platform and collaborate on other cloud platforms (Oracle Cloud, AWS) to maintain scalability and ensure secure data access.

Data Quality Assurance: Conduct regular quality checks, clean data sets, and implement data governance practices to maintain accuracy and reliability.

Documentation & Reporting: Keep thorough documentation of data processes, configurations, and workflows. Provide regular updates on system performance and data availability to downstream users and program director.

Innovation & Continuous Improvement: Stay up to date on industry trends, experiment with new data tools and techniques, and apply best practices to drive continuous improvement.

AI/ML Integration: Support AI/ML initiatives to enhance and support data processes, predictive modeling, and drive business value for business units.