Data Architect – Snowflake/Databricks

About Systango

Role Overview

Key Responsibilities

• Data Architecture: Define end-to-end data architecture, including data lakes, lakehouses, modern data warehouses, data marts, and enterprise data platforms.
• Snowflake & Databricks: Design and optimize data solutions using Snowflake OR Databricks, selecting appropriate architectural patterns based on business and technical requirements.
• Data Engineering: Architect scalable batch and streaming pipelines, ETL/ELT workflows with medallion architecture, data transformations using dbt, and orchestration frameworks.
• Cloud Platforms: Design data solutions on AWS, Azure, or GCP, integrating cloud storage, compute, security, and networking services.
• Data Modelling: Define conceptual, logical, and physical data models, including dimensional modelling, Data Vault 2.0, and normalized data structures.
• Performance & Cost Optimization: Optimize query performance, compute utilization, storage, workload management, and overall platform costs.
• Data Governance & Security: Establish standards for data quality, lineage, metadata management, access control, encryption, privacy, and regulatory compliance.
• Architecture Standards: Define reusable architecture patterns, engineering best practices, coding standards, CI/CD processes, and data platform design principles.
• Technical Leadership: Mentor data engineers and technical leads, conduct architecture reviews, and guide teams through complex technical decisions.
• Stakeholder Collaboration: Work with product owners, business stakeholders, and clients to understand requirements and communicate architectural decisions effectively.
• AI & Analytics Enablement: Build data foundations that support BI, advanced analytics, machine learning, and Generative AI applications, including RAG and vector search use cases where applicable.

Required Skills & Experience

• 10–15 years of experience in data engineering, data architecture, or enterprise data platforms.
• Strong hands-on expertise in Snowflake/Databricks, including architecture, implementation, optimization, and production-scale deployments.
• Excellent proficiency in SQL and Python, with experience in PySpark and distributed data processing.
• Strong understanding of modern data architecture patterns, including lakehouse, data warehouse, data lake, and medallion architecture.
• Proven experience designing enterprise-grade ETL/ELT pipelines and scalable data integration frameworks.
• Experience with cloud platforms such as AWS, Microsoft Azure, or GCP.
• Strong understanding of data modelling, dimensional modelling, Data Vault, and data warehouse design principles.
• Must have strong hands-on experience with Kafka, real-time data streaming, event-driven architectures, and high-throughput distributed data processing.
• Experience with orchestration tools such as Airflow, Azure Data Factory, or equivalent platforms.
• Familiarity with data governance, data quality frameworks, lineage, metadata management, and enterprise security.
• Experience with Git, CI/CD, infrastructure as code, and automated data deployment practices.
• Demonstrated ability to lead architecture discussions, mentor engineers, and make sound technical decisions.
• Proven experience building and mentoring Data Engineering teams, including junior engineers, through technical guidance, code reviews, and best-practice implementation.
• Must hold at least one valid Snowflake or Databricks certification.
• Must have hands-on experience designing and optimizing production data platforms processing petabytes of data.
• Excellent communication, problem-solving, and stakeholder management skills.

Good to Have

• Experience with dbt, Delta Lake, Unity Catalog, Snowflake Dynamic Tables, or Snowpark.
• Knowledge of Kafka, Spark Structured Streaming, or other real-time data processing technologies.
• Experience with data mesh, domain-driven data architecture, or multi-tenant data platforms.
• Experience building data platforms for AI/ML, GenAI, feature engineering, and vector search.
• Exposure to FinTech, payments, financial services, or other data-intensive industries.

What Success Looks Like

• Designing scalable, secure, and cost-efficient data platforms aligned with business objectives.
• Establishing architecture standards that improve data reliability, quality, and engineering productivity.
• Enabling faster delivery of analytics, reporting, and AI-driven solutions.
• Providing technical direction and raising the overall architecture and engineering maturity of the team.