Senior Data Architect

OVERVIEW OF 73 STRINGS:

73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The company's AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit.

Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures.

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 Do

• 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.