Senior Technical Lead

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