Senior Snowflake Engineer
We are looking for an experienced Senior Snowflake Engineer with strong Master Data Management (MDM) architecture experience to join the EDH team. This role will be responsible for designing, developing, and supporting enterprise-scale data solutions using Snowflake, AWS, DBT, and modern data integration technologies, with a strong focus on MDM, data quality, data governance, and trusted enterprise data.
The ideal candidate will have deep hands-on experience with Snowflake and modern cloud data engineering, combined with a strong understanding of MDM architecture, master data domains, entity resolution, survivorship, matching, golden records, data quality, and data governance. The candidate should be able to translate MDM business requirements into scalable technical solutions and work across data engineering, architecture, governance, and downstream consuming systems.
Key Responsibilities:
- Design, develop, and support enterprise-scale data pipelines using AWS, Snowflake, DBT, and related data engineering technologies.
- Design and implement scalable MDM and master data architecture within the enterprise data platform.
- Define technical solutions for managing master data domains such as Customer, Account, Product, Supplier, Organization, Location, or other enterprise entities.
- Design processes for entity identification, matching, merging, survivorship, golden record creation, and master data distribution.
- Develop data models and structures to support master data, reference data, relationships, hierarchies, and cross-reference mappings.
- Implement MDM solutions using Snowflake and surrounding data platform components while maintaining clear separation between master, transactional, analytical, and reference data.
- Develop and enhance metadata-driven data ingestion, transformation, and MDM processing frameworks.
- Design data pipelines to ingest and consolidate data from multiple source systems into trusted master data structures.
- Implement data quality rules, validation, standardization, cleansing, deduplication, and exception management processes.
- Develop mechanisms to establish and maintain golden records and trusted enterprise identifiers.
- Design and implement source-to-master and master-to-consumer data flows, including downstream distribution and synchronization requirements.
- Support MDM-related integration with operational systems, analytical platforms, APIs, and enterprise applications.
- Develop DBT models, macros, tests, and deployment configurations for data transformation and MDM processing.
- Implement Snowflake solutions including database design, data loading, transformation, security, performance optimization, and operational support.
- Troubleshoot and debug data pipeline and MDM processing issues across AWS, Snowflake, DBT, and PySpark.
- Perform root cause analysis of data quality, matching, integration, and master data issues.
- Establish monitoring and operational processes for data quality, MDM pipelines, master data completeness, and data reconciliation.
- Collaborate with data architects, business stakeholders, data governance teams, and engineering teams to define enterprise data standards and MDM requirements.
- Provide technical leadership, conduct code reviews, and mentor other engineers.
- Participate in architecture discussions and recommend improvements to the enterprise data and MDM platform.
- Support CI/CD, deployment automation, environment promotion, and production releases.
Required Technical Skills:
Snowflake: Strong hands-on experience with Snowflake architecture and development, including database design, data modeling, data loading, transformation, performance tuning, security, and optimization.
MDM Architecture: Strong experience designing and implementing Master Data Management solutions and architectures. Understanding master data domains, golden records, entity resolution, matching and merging, survivorship, hierarchies, relationships, cross-reference identifiers, and master data distribution.
Data Engineering: Strong hands-on experience building enterprise data pipelines and data integration solutions using cloud-based data platforms.
AWS: Strong hands-on experience with AWS data services and building scalable cloud data ingestion and processing solutions.
DBT: Strong hands-on experience developing DBT models, macros, tests, documentation, and deployment processes.
Data Modeling: Strong experience with conceptual, logical, and physical data modeling, including dimensional models, normalized models, master data models, reference data, hierarchies, and relationships.
Data Quality: Experience implementing data quality frameworks including profiling, validation, standardization, cleansing, deduplication, reconciliation, and exception handling.
SQL: Strong SQL skills including complex queries, data transformation, performance tuning, and optimization.
PySpark: Experience developing PySpark applications for data ingestion, transformation, cleansing, and large-scale data processing.
Data Integration: Experience integrating data from multiple enterprise source systems and designing scalable source-to-target data flows.
DevOps / CI/CD: Experience with Git, CI/CD pipelines, deployment automation, release management, and environment promotion.
