Anblicks
Key Responsibilities
* Lead the end-to-end architecture and implementation of Snowflake (Bronze/Silver/Gold layers).
* Partner with business and data stakeholders to translate requirements into canonical models, ontologies, and data products .
* Define and enforce data modeling standards , naming conventions, and domain-driven design principles.
* Guide teams on data ingestion patterns , transformation frameworks (e.g., dbt), and performance optimization in Snowflake.
* Integrate data management capabilities including Data Quality using SODA, Data Governance, Metadata Management, and Data Observability.
* Ensure platform scalability, security, cost optimization, and compliance with enterprise standards.
* Provide technical leadership and mentorship to data engineers and modelers.
* Act as a key contributor in roadmap planning, technical decision-making, and stakeholder communication.
Required Skills & Experience
* 10+ years of experience in data engineering and platform architecture , with at least 3+ years in a lead role.
* Strong hands-on experience with Snowflake (performance tuning, clustering, security, cost optimization).
* Hands-on experience with cloud platforms (AWS, Azure, or GCP)
* Understanding with Data Domains (Client, Finance etc.)
* Deep understanding of data modeling (dimensional, canonical, domain-driven).
* Experience designing or working with ontology / semantic layers (business vocabularies, relationships, metrics).
* Strong knowledge of modern data stack tools (dbt, orchestration, CI/CD for data).
* Experience implementing Data Quality, Data Governance, Metadata, and Data Observability solutions.
* Experience with orchestration tools such as: Apache Airflow, Prefect, or Luigi.
* Solid SQL skills and strong understanding of ELT patterns.
* Ability to lead cross-functional teams and communicate complex concepts to both technical and non-technical stakeholders.
Nice to Have
* Experience with knowledge graphs, semantic models, or graph technologies .
* Exposure to enterprise data platforms supporting multiple domains and global users.
* Background in cloud-native architectures and large-scale data modernization programs.
* Lead the end-to-end architecture and implementation of Snowflake (Bronze/Silver/Gold layers).
* Partner with business and data stakeholders to translate requirements into canonical models, ontologies, and data products .
* Define and enforce data modeling standards , naming conventions, and domain-driven design principles.
* Guide teams on data ingestion patterns , transformation frameworks (e.g., dbt), and performance optimization in Snowflake.
* Integrate data management capabilities including Data Quality using SODA, Data Governance, Metadata Management, and Data Observability.
* Ensure platform scalability, security, cost optimization, and compliance with enterprise standards.
* Provide technical leadership and mentorship to data engineers and modelers.
* Act as a key contributor in roadmap planning, technical decision-making, and stakeholder communication.
Required Skills & Experience
* 10+ years of experience in data engineering and platform architecture , with at least 3+ years in a lead role.
* Strong hands-on experience with Snowflake (performance tuning, clustering, security, cost optimization).
* Hands-on experience with cloud platforms (AWS, Azure, or GCP)
* Understanding with Data Domains (Client, Finance etc.)
* Deep understanding of data modeling (dimensional, canonical, domain-driven).
* Experience designing or working with ontology / semantic layers (business vocabularies, relationships, metrics).
* Strong knowledge of modern data stack tools (dbt, orchestration, CI/CD for data).
* Experience implementing Data Quality, Data Governance, Metadata, and Data Observability solutions.
* Experience with orchestration tools such as: Apache Airflow, Prefect, or Luigi.
* Solid SQL skills and strong understanding of ELT patterns.
* Ability to lead cross-functional teams and communicate complex concepts to both technical and non-technical stakeholders.
Nice to Have
* Experience with knowledge graphs, semantic models, or graph technologies .
* Exposure to enterprise data platforms supporting multiple domains and global users.
* Background in cloud-native architectures and large-scale data modernization programs.