This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're seeking a strategic and hands-on Data Architect to design and implement scalable, secure, and modern data solutions across our organization. You'll play a critical role in leading the modernization of our data ecosystem using dbt and Snowflake, building a foundation for real-time analytics and enterprise-scale reporting in the cloud. - Architect and evolve our data platform using Snowflake as the central data warehouse and dbt for transformation logic. - Design and implement batch and streaming pipelines, ensuring scalability and cost-efficiency in AWS. - Establish best practices for data modeling, warehouse schema design, and semantic layer standardization using dbt. - Define and enforce data governance, security, and privacy policies in alignment with compliance frameworks. - Lead data migration from legacy sources to Snowflake; work closely with DevOps to automate and secure deployment processes. - Collaborate with engineering, product, and business stakeholders to understand data needs and align solutions with business goals. - Mentor and guide data engineers; support code reviews, architectural reviews, and team upskilling. Qualifications - 8+ years in data architecture, with deep experience in cloud-based data platforms. - Strong hands-on experience with Snowflake and dbt (must-have). - Expertise in data modeling (3NF, star/snowflake schemas), ELT/ETL design, and performance tuning. - Solid understanding of AWS services (S3, Glue, Lambda, Redshift, Kinesis, etc.). - Strong programming in SQL and Python; experience with CI/CD and data testing in dbt. - Knowledge of data governance, lineage, metadata management, and data quality frameworks. - Experience working with streaming technologies (Kafka, Kinesis) and large-scale datasets. Preferred - Experience in platform modernization and cloud migration projects. - Familiarity with tools like Airflow, Terraform, Great Expectations, or Monte Carlo. Company Description Street Context is an entity of BlueMatrix. Together they develop one-of-a-kind web applications for the authoring, distribution, and analysis of investment research, and for internal knowledge management and digital communications. Our mission is to streamline the publishing process on a global scale. The BlueMatrix and Street Context teams are made up of ambitious and passionate people who have turned technology development and client service into an art. Many have dedicated the majority of their career to growing the BlueMatrix brand and helping shape the investment research marketplace. With locations in Toronto, New York City, Durham, London, Edinburgh, and Timisoara, Romania, we collaborate across the globe for a unique perspective on the impact of technology in our industry. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

• Design, build, and own scalable data pipelines for analytics and reporting. • Integrate datasets from APIs, vendors, and event streams into the data warehouse. • Maintain clear documentation for data models, pipelines, and system architecture. • Write high-quality SQL and optimize database performance across large datasets. • Troubleshoot data issues, including pipeline failures, schema changes, and data-quality gaps. • Define and maintain core business metrics used across analytics, finance, and product. • Analyze user behavior and funnels to support product decisions and growth initiatives. • Handle ad-hoc data requests, analyses, and exploratory investigations.

• Progettare e sviluppare pipeline dati complesse e controlli di data quality • Contribuire all'analisi tecnica e funzionale dei progetti • Creare e aggiornare modelli di dati (concettuali, logici, fisici) • Documentare rilasci, installazioni e manutenzioni software • Guidare un team e partecipare a riunioni con il cliente

• Architecting robust data pipelines across on-prem and cloud Oracle ecosystems • Automating ingestion from varied sources including APIs