Lead Data Engineer
Remote
6+ Months
Mandatory:
Snowflake with Matillion
Required Skills
10+ years of experience with expertise in solutioning Data Warehouse platform, architecting designing complex Data Analytics projects
Experience in modernization migration from legacy Data Warehouse platform from Netezza, DataStage into Azure cloud native Snowflake, Matillion DBT platform Metadata and business glossary management, and data quality.
Experience in creating Data Flow Diagrams (DFD) Healthcare Payer Industry experience in designing the logical and physical data models for the Datawarehouse as per business requirements
In-depth knowledge of Azure Cloud data capabilities and services and how those relate to the development migration activities.
Must have hands-on experience (developmentmigration) on Azure Data Lake Gen2, Snowflake, Matillion, DBT
Experience on migrating large Datahub to Cloud. Experience on decommissioning co-ordination after migration for Source systems.
Good understanding of DevOps and Data pipeline automation platforms
Familiarity with legal and regulatory requirements relevant to cloud data architecture, such as SOX, PCI, HIPAA, and Data Protection (GDPR, DPDP 2023)
Familiarity with cryptographic algorithms and security protocols.
Skilled in designing and building distributed systems for processing large data volumes.
Excellent communication, presentation, and interpersonal skills are essential.
Responsibilities Lead andor assist in designing and developing cloud-based data systems, tailoring solutions to meet business requirements.
Develop comprehensive architecture solution migration roadmaps seamlessly integrating business processes and technologies.
Design, develop and enforce standards for data migration, storage, processing, and governance across all environments.
Work closely with enterprise and application architects to align the data engineering team to the overall company SDLC standards, practices, and data access patterns.
Develop, document, and maintain overall view of data platform architecture, data acquisition, data quality, and data retention.
Provide guidance in migration of ETL jobs including developing best practices Provide formal and informal training for data engineers, platform engineers and ETL developers.
Maintain knowledge of emerging technologies and architectures
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