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 looking for a Senior Data Engineer to build and own the data pipelines, integrations, and warehousing infrastructure that power 73 Strings' valuation and monitoring platform. This is a hands-on engineering role — you will design, develop, and maintain scalable data solutions that ingest financial data from multiple sources, transform it for downstream analytics, and keep it reliable in production.
You will work directly with product, engineering, and client-facing teams to translate complex financial data requirements into well-engineered, observable pipelines. The role demands deep technical execution, not just architectural direction.
What You’ll Do
Data Engineering & Pipeline Development
Design, develop, and maintain end-to-end ETL/ELT pipelines that ingest financial and valuation data from relational databases, APIs, and Elasticsearch.
Implement Change Data Capture (CDC) and incremental loading strategies to keep data fresh without full reloads.
Build scalable ingestion frameworks for dynamic and evolving schemas, including semi-structured JSON payloads.
Troubleshoot and resolve complex data integration issues in production through structured root cause analysis.
Develop and maintain data transformation logic using dbt and Apache Airflow for orchestration.
Cloud Data Warehousing
Build and maintain dimensional models, fact tables, and reporting data marts on cloud data warehouse platforms.
Optimize warehouse performance through query tuning, partitioning strategies, and storage cost management.
Develop and manage data pipelines using cloud-native warehousing features for ingestion, transformation, and change tracking.
Maintain transformation logic supporting analytical reporting and downstream BI consumption.
API & Integration Development
Build and maintain integrations from REST APIs, SaaS platforms, and internal applications into the data platform.
Design reusable ingestion patterns that handle authentication, pagination, rate limiting, and schema drift.
Collaborate with product and engineering teams to define integration contracts and data schemas.
Work with JSON and other semi-structured formats; ensure clean parsing and lineage back to source.
Data Quality & Governance
Implement validation frameworks, reconciliation checks, and data quality rules across pipelines.
Set up monitoring, alerting, and auditing so data issues surface before they reach end users.
Ensure data accuracy, completeness, and consistency across systems and reporting layers.
Participate in data governance practices: lineage tracking, documentation, and access controls.
Stakeholder Collaboration
Work directly with business users, valuation teams, product managers, and client stakeholders to gather requirements.
Translate business and regulatory data requirements into clear, implementable technical designs.
Support production deployments, incident resolution, and ongoing pipeline enhancements.
Communicate clearly with both technical teammates and non-technical stakeholders on data issues and delivery timelines.
Requirements:
Experience
5+ years in data engineering, ETL development, or data integration — with production systems, not just prototypes.
Proven track record delivering enterprise data pipelines in cloud environments.
Experience with Snowflake or Databricks as a primary data platform: dimensional modeling, performance tuning, and cost management.
Hands-on Informatica IICS development: mappings, tasks, REST API connectors, and error handling.
Experience integrating data from REST APIs, relational databases (PostgreSQL, SQL Server, MySQL), and semi-structured sources.
Practical knowledge of dbt for transformation logic and Apache Airflow for orchestration.
Technical Skills
ETL/ELT design: CDC patterns, incremental loads, full reloads, and SCD handling.
Python or Scala for data processing, scripting, and pipeline automation.
REST API consumption: authentication (OAuth, API keys), pagination, rate limiting, and schema mapping.
JSON and semi-structured data parsing; experience handling evolving or inconsistent schemas.
Streaming and real-time data: working knowledge of Kafka or Spark Streaming (production experience a plus).
Cloud data platform experience: AWS (Glue, S3, Lambda), Azure (Data Factory, ADLS), or GCP (Dataflow, BigQuery).
Data quality tooling: validation frameworks, reconciliation checks, pipeline observability, and alerting.
SQL proficiency: complex joins, window functions, CTEs, and query optimization.
Skills Required
- 8+ years of experience in data architecture or data engineering.
- Expertise in Snowflake and Databricks including data modeling, performance tuning, and cost optimization.
- Hands-on experience designing and building REST APIs and ETL/ELT pipelines at scale.
- Proficiency with real-time and streaming platforms such as Kafka, Flink, and Spark.
- Hands-on experience with dbt and Apache Airflow.
- Experience with data testing frameworks, pipeline observability, and monitoring practices.
- Experience with major cloud platforms (AWS, Azure, or GCP) and cloud-native data services.
- Proven experience with vector databases or embedding infrastructure in production.
- Experience integrating unstructured data (documents, PDFs, presentations) into a structured data platform, including extraction, normalization, and lineage.
- Knowledge of data governance, security, and compliance (e.g., SOC 2, GDPR, CCPA).
- Working understanding of LLM and RAG architectures, including tenant-aware retrieval and context isolation.
- Demonstrated ability to drive technical strategy and lead cross-functional projects.
- Strong communication skills to translate complex technical concepts for executive and non-technical audiences.
What We Do
73 Strings is a global fintech firm that provides an AI-augmented platform for the alternative asset management industry. Its solutions, such as Qubit X and Graviton X, streamline data extraction, portfolio monitoring, and valuation processes for illiquid assets. By automating these complex tasks, the company enables investment professionals to focus on making informed judgments and acting on critical insights, ultimately improving efficiency and decision-making across private capital markets.
