Data Architect AWS & Snowflake
Data Architect AWS & Snowflake
synectics apac- Posted an hour ago
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Job Description
Key Responsibilities
Data Architecture & Engineering
Data Architecture & Engineering
- Design and implement scalable, secure, and high-performance cloud data architectures on AWS.
- Define data architecture standards, patterns, frameworks, and best practices across the organization.
- Design and develop robust data pipelines and ETL/ELT workflows for batch and near-real-time data processing.
- Build and optimize enterprise data warehouses and analytical data platforms using Snowflake.
- Design data integration solutions across multiple internal and external data sources.
- Implement Change Data Capture (CDC) and incremental data processing patterns.
- Ensure data platforms are scalable, reliable, maintainable, and cost-efficient.
- Design conceptual, logical, and physical data models.
- Develop dimensional models using Kimball methodology, including fact and dimension tables.
- Define data marts and analytical models supporting business reporting and analytics.
- Design and maintain data structures that support both operational and analytical workloads.
- Establish data modelling standards and governance practices.
- Develop and maintain production-grade pipelines using Python and PySpark.
- Build and manage workflow orchestration using Apache Airflow.
- Develop transformation frameworks using dbt.
- Optimize SQL queries, Snowflake workloads, data processing jobs, and pipeline performance.
- Implement monitoring, logging, alerting, error handling, and data quality checks across pipelines.
- Design and maintain the semantic/data consumption layer for BI and analytics.
- Work closely with BI and business teams to create trusted, reusable datasets and metrics.
- Ensure consistent definitions and business logic across reporting and analytical use cases.
- Support self-service analytics by providing well-structured and governed data products.
- Partner with AI/ML teams to design data foundations for machine learning, GenAI, and analytical products.
- Support data pipelines and architectures for LLM/RAG-based applications and AI data products.
- Ensure data platforms can support high-volume data processing and evolving AI/ML workloads.
- Contribute to data architecture patterns for model training, feature generation, retrieval, and inference workflows.
- Use modern AI-powered development tools such as GitHub Copilot, Cursor, Claude Code, or equivalent to improve engineering productivity.
- Apply AI-assisted development responsibly for coding, testing, debugging, documentation, refactoring, and technical analysis.
- Identify opportunities to incorporate AI into engineering workflows and accelerate delivery without compromising quality or security.
- Collaborate with Data Engineering, Data Science, BI, Product, and business stakeholders.
- Translate business requirements into scalable technical data solutions.
- Provide technical leadership and mentorship to data engineers and other technical team members.
- Conduct architecture reviews and establish engineering best practices.
- Troubleshoot complex data architecture and pipeline issues and drive them through to resolution.
- 12-17 years of overall experience in Data Engineering, Data Architecture, or related fields.
- Strong hands-on expertise in AWS data services and cloud architecture.
- Expert-level experience with Snowflake, including architecture, performance optimization, data modelling, and warehouse design.
- Expert-level SQL skills.
- Strong programming experience with Python.
- Strong experience with PySpark and distributed data processing.
- Hands-on experience with Apache Airflow for workflow orchestration.
- Hands-on experience with dbt for data transformation and modelling.
- Strong experience in ETL/ELT, data integration, CDC, and pipeline development.
- Strong knowledge of modern data warehousing and dimensional modelling.
- Hands-on experience with Kimball dimensional modelling methodology.
- Experience designing semantic/BI layers and analytical data models.
- Strong understanding of data quality, governance, security, monitoring, and performance optimization.
- Experience designing scalable data platforms supporting large volumes of data.
- Hands-on experience with AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar.
More Info
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Key Skills
Dimensional modelling
AWS data services
Data quality governance
About Company
Skills:
snowflake , Data Warehousing, Metadata Management, AWS, Streaming, Data Quality, Data Governance, Data Modeling, Generative AI, Atlan, Argo CD, Data Catalog, Real-time s, Dimensional Data Vault, MDM