Data Architect

Data / ETL Architect

Experience: 14+ years

Skills : Databricks, ETL, Pyspark, Python, Presales

Requirements

• 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.