Senior Consultant

Role Overview We are looking for a skilled Data Modeler/Data Engineer to design, build, and maintain scalable enterprise data platforms. The role involves working with business teams, data engineers, and analytics teams to create efficient data models, improve data quality, and support cloud-based data solutions. Key Responsibilities Design conceptual, logical, and physical data models for enterprise platforms. Develop and maintain data warehouses, data lakes, and lakehouse solutions. Work with business and technical teams to gather requirements and convert them into scalable data solutions. Perform data analysis, profiling, validation, and quality checks. Support ETL/ELT processes and data integration across multiple systems. Optimize database performance using query tuning, indexing, and partitioning. Maintain metadata, data lineage, and technical documentation. Support data governance, master data management, and compliance initiatives. Ensure compliance with standards such as HIPAA, GDPR, and GxP where applicable. Collaborate in Agile/Scrum teams and support CI/CD deployment processes. Participate in innovation initiatives and platform enhancements. Required Skills Strong experience in data modeling, data engineering, and enterprise data architecture. Expertise in relational and dimensional modeling including Star and Snowflake schema. Good understanding of data warehouses, data lakes, and lakehouse architectures. Hands-on experience with SQL Server, Oracle, PostgreSQL, Snowflake, or Redshift. Strong SQL and query optimization skills. Experience with ETL/ELT tools such as DBT, Informatica, Talend, Azure Data Factory, or AWS Glue. Experience with cloud platforms like AWS, Azure, or GCP. Knowledge of AWS services including Glue, S3, Redshift, Lambda, Athena, and EMR. Familiarity with Git, Jenkins, GitHub Actions, or Azure DevOps. Experience with data governance and metadata tools like Collibra or Alation. Experience working in Agile/Scrum environments. Preferred Skills Experience with Spark, Databricks, Hadoop, or Kafka. Knowledge of Python or PySpark. Exposure to Power BI, Tableau, or QuickSight. Experience with APIs, streaming pipelines, Docker, Kubernetes, Terraform, or CloudFormation. Understanding of Data Vault 2.0 concepts. Experience in Pharma, Healthcare, or Life Sciences domain.