Senior Data Architect

73 Strings•Toronto, ON

12d

About The Position

73 Strings is 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 where you will own architectural direction, drive integration strategy, and act as a trusted technical advisor to both internal stakeholders and external clients. The company is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. Their AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit.

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, normalization, and lineage back to source artifacts.
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.

Responsibilities

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).
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.
Serve as a technical expert and Engineering partner to pre-sales and implementation teams providing architectural guidance where needed to ensure successful client onboarding.
Engage with critical prospects and clients as needed, helping build trusted relationships with their senior technical stakeholders.