Job Description

TECHNICAL SKILLS

Demonstrated experience enterprise cloud data engineering, design, and data management techniques and principles related to

data warehousing, operations data stores, data marts, data lake design, and other

emerging technologies. Proven expertise in designing and orchestrating batch and real-time data ingestion workflows using

Azure Data Factory (ADF)

and other ETL tools such as Informatica IICS, Snowflake, AWS Glue etc. Hands-on expertise in

building, testing, deploying, and monitoring

scalable ETL/ELT pipelines using Azure Data Factory and related Azure services. Proficient in data pipelines troubleshooting, development of

triggers, automatic failure notification, optimizing, and cost-tuning Azure-based

data pipelines and cloud workflows. Hands-on experience ingesting data from a wide range of cloud and web applications, such as

Meta Ads, Google Ads, GA4, BigQuery, Salesforce, and REST APIs

using Azure Data Factory. Advanced proficiency in

SQL for querying, managing, and optimizing datasets

within Azure SQL Database and Azure Synapse Analytics. Experience working with

Azure Data Lake and Azure Blob Storage

to manage structured and unstructured data assets. Ability to develop robust pipelines to ingest data from

databases, sFTP, file-based systems, and external APIs

within the Azure ecosystem. Strong knowledge of data modeling, including

conceptual, logical, and physical model

design for both transactional and analytical systems. Strong expertise on Azure data and AI ecosystem, with proficiency in services such as

Azure Data Lake, Azure Synapse Analytics, Azure ML, Azure SQL Database

, and seamless integration of ADF pipelines with

AI/ML workflows

.

Experience developing and operationalizing AI/ML pipelines

, including preparing training datasets, orchestrating model training/inference, and integrating ML models into data workflows using ADF, Azure ML, Databricks, or Synapse ML. Practical experience with

data normalization, denormalization, relational database design,

and using data modeling tools such as ER/Studio, Erwin, or SQL Power Architect. Demonstrated ability to implement and

uphold coding standards, manage code reviews, and lead data

validation and testing (unit, integration, and regression). Proven experience establishing

data standards, naming conventions, and governance for enterprise

and clinical datasets. Extensive knowledge of healthcare data vocabularies such as

SNOMED CT, LOINC, ICD-10, and RxNorm

are a strong plus. Solid experience implementing

CI/CD pipelines, including branching strategies, version control (e.g., Git),

and automated deployments. Experience developing and maintaining

data quality frameworks, including data profiling, validation, and cleansing

strategies. Proven ability to drive

metadata management and data lineage practices that support auditability, compliance, and governance.

Strong collaboration skills with

business, clinical, and technical stakeholders

to align data initiatives with organizational goals and regulatory requirements (e.g., HIPAA, GDPR). Ability to manage

project timelines, prioritize tasks

, adapt to changing requirements, and deliver high-quality data solutions on time.

Requirements

Over 5 years of experience in designing and implementing

conceptual, logical, and physical data models in both transactional and analytical environments for healthcare organizations.

Over 5 years of recent hands-on experience in designing and implementing cloud data engineering solutions in the

healthcare domain

. Over 8 years of experience in Deep understanding of

data normalization and denormalization principles

, relational database design, and performance optimization. Over 10 Years of experience in developing cloud

data warehousing, data lake, data marts and centralized data

ecosystem using various ETL tools and database management system. Over 8 years of experience in translating

complex business and clinical requirements into scalable data structures that support reporting, analytics, and interoperability

. Over 5 years of experience in healthcare industry-standard healthcare data models

(e.g., CDISC SDTM/ADaM, OMOP, FHIR, HL7).

Over 7 years of experience in developing, automating, and optimizing scalable data pipelines using

Azure Data Factory, Azure Synapse Analytics, and Azure Databricks

to ingest, transform, and load data from various sources into cloud-based data lakes and warehouses. Over 7 years of experience in Implementing

robust data models and architecture

using Azure SQL Database, Azure Lake Storage Gen2, and Delta Lake to support analytics, BI, and machine learning cases. Over 7 years of experience in

developing and maintaining ADF pipelines, data flows, and integration runtimes

, ensuring reliable and scalable data ingestion into Azure Data Lake, Azure SQL, and Synapse Analytics. Over 5 years of experience in master data management & data quality, metadata management, data lineage, business glossaries & definition documentation, ensuring transparency and traceability of model elements. Over 7 years of experience in designing and developing complex

Azure Data Factory (ADF) pipelines

to orchestrate data ingestion, transformation, and loading (ETL/ELT) across hybrid data sources including

SQL Server, REST APIs, Blob Storage, and third-party services.

Over 3 years of experience designing and implementing

AI/ML pipelines and training data models

for analytics engines using a variety of cloud-based analytics tools

Qualifications

Bachelor's Degree

Range of Year Experience-Min Year

8

Range of Year Experience-Max Year

10