Job Title

Data / ETL ArchitectExperience: 14+ yearsSkills : Databricks, ETL, Pyspark, Python, PresalesRequirements- Minimum eight years of relevant experience as a data architect or data engineer building large-scale data solutions. - P&C domain experience a must. - Bachelor’s degree in engineering, Information Technology, Computer Science, or a related field. - Experience in architecting and large data modernization, data migration, data warehousing – experience with cloud-based data platforms (like Snowflake). - Experience with defining and operationalizing data strategy, data governance, data lineage and quality standards. - Extensive knowledge of data engineering, data integration and data management concepts (i.e. APIs, ETL, MDM, CRUD, Pub/Sub, etc.) - Experience with data modelling. - Experience with structured and hierarchical datasets (i.e. JSON, XML, etc.) - Engineering experience with large scale system integration and analytics projects - Consulting mindset – highly collaborative, highly communicative approach with an eye on influence, rather than control. - Ability to work on high-level strategy and low-level tactical integration along with stakeholders at all levels of the organization. - Ability to communicate complex systems and concepts through pictures. - Clear and concise communication skills – both written and oral. - Remains unbiased to specific technology or vendor – more interested in results.- Should have 15+ years of experience with last 4 years in implementing Cloud native Data Solutions for variety of data consumption needs such as Modern Data warehouse, BI, Insights and Analytics - Should have experience in architecture and implementing End to End Modern Data Solutions using AWS and advance data processing frameworks like Databricks etc. - Strong knowledge of cloud native data platform architectures, data engineering and data management - Good knowledge of popular database and data warehouse technologies from Snowflake and AWS - Demonstrated knowledge of data warehouse concepts. Strong understanding of Cloud native databases, columnar database architectures - Ability to work with Data Engineering teams, Data Management Team, BI and Analytics in a complex development IT environment. - Good appreciation and at least one implementation experience on processing substrates in Data Engineering - such as ETL Tools, Confluent Kafka, ELT techniques - Exposure to varying databases – NoSQL (at very minimum Key value stores and/or Document stores), Appliances. Be able to cite implementation experiences constraints and performance challenges in practice. - Preferable (Nice to have): Implementing analytic models using AWS SageMaker for production workloads. - Data Mesh and Data Products designing, and implementation knowledge will be an added advantage.