Senior Data Engineer

Stack

Responsibilities

• ▹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
• ▹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
• ▹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
• ▹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
• ▹Work directly with business users, valuation teams, product managers, and client stakeholders to gather requirements
• ▹Support production deployments, incident resolution, and ongoing pipeline enhancements

Requirements

• ▹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
• ▹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
• ▹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

Nice to have

• ▹Working knowledge of Kafka or Spark Streaming (production experience a plus)

Soft skills

About the company

73 Strings is an innovative, AI-powered platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry, streamlining middle-office processes for alternative investments — data structuring, standardization, monitoring, and fair value estimation. It serves clients globally across Private Equity, Growth Equity, Venture Capital, Infrastructure, and Private Credit. Its 2025 $55M Series B — the largest in the industry — was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, and continued support from Blackstone, Fidelity International Strategic Ventures, and Broadhaven Ventures.