About the Role:We are seeking a Senior Data Architect to serve as a technical lead shaping the future of our data platform in the financial services space. This is a high-impact, hands-on leadership role - you will own architectural direction, drive integration strategy, and act as a trusted technical advisor to both internal stakeholders and external clients.
What You'll DoData Platform Leadership
- Define and evolve the enterprise data architecture strategy, ensuring scalability, reliability, and governance across the platform.
- Lead the design and implementation of cloud data warehousing and lakehouse solutions, with a focus on Snowflake and Databricks, aligned with financial services data requirements.
- Establish data modeling standards, data quality frameworks, and best practices across engineering teams.
- Champion data governance, security, and compliance practices in alignment with financial industry regulations (e.g., SOC 2, GDPR, CCPA).
Integrations & API Development
- Partner with Product Management and business stakeholders to design and deliver robust data integrations and APIs (REST, GraphQL, ETL/ELT pipelines).
- Architect scalable, reusable integration patterns that connect internal systems, third-party platforms, and client data ecosystems.
- Define API contracts, data schemas, and integration standards that support both internal development teams and external partners.
- Translate complex business and regulatory requirements into sound, implementable technical designs.
Implementation Support
- Serve as a technical expert and Engineering partner to pre-sales and implementation teams providing architectural guidance where needed to ensure successful client on boarding
- Engage with critical prospects and clients as needed, helping build trusted relationships with their senior technical stakeholders.
Requirements:
- 8+ years of experience in data architecture or data engineering.
- Proven expertise in cloud data platforms such as Snowflake, Databricks, including data modeling, performance tuning, and cost optimization.
- Hands-on experience designing and building REST APIs and ETL/ELT pipelines at scale.
- Strong proficiency with real-time and streaming data platforms such as Kafka, Flink, and Spark.
- Hands-on experience with modern data orchestration and transformation tools such as dbt and Apache Airflow.
- Experience with data testing frameworks, pipeline observability, and monitoring practices that ensure data quality, reliability, and operational visibility in production environments.
- Experience with major cloud platforms (AWS, Azure, or GCP), including cloud-native data services, networking, and security.
- Proven experience with vector database or embedding infrastructure in production.
- Experience integrating unstructured data (documents, PDFs, presentations) into a structured data platform, including extraction, normalisation, and lineage back to source artefacts.
- Demonstrated ability to drive technical strategy and lead cross-functional projects.
- Working understanding of LLM and RAG architectures, including tenant-aware retrieval, context isolation, and the data quality and lineage prerequisites for safe deployment.
- Strong communication skills with the ability to translate complex technical concepts for executive and non-technical audiences.
Department Technology Locations Toronto
