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73 Strings
73 Strings
51 – 200 Employees
Artificial IntelligenceFinanceFintech
73 Strings is a global fintech company developing augmented-intelligence technology for the analysis, valuation, and monitoring of illiquid assets. Its platform brings together data extraction, portfolio monitoring, and valuation workflows to help alternative asset managers turn complex financial data into timely, actionable insight. The company serves firms across private equity, venture capital, private credit, and infrastructure, supporting more efficient and informed valuation processes through AI-driven analytics.
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Senior Data Engineer - London Hybrid

Lead the design and operation of Azure data pipelines and warehouses for 73 Strings’ private-capital valuation platform. Own ingestion, transformation, data quality, CI/CD, and client data delivery.

GBUnited KingdomHybridFull-timeSeniorEnglish RequiredData Engineer
AirflowApacheAzureKafkaMS SQL ServerPythonSparkSQL

Description

  • Shape the platform architecture across ingestion, processing, and delivery
  • Build and run batch and streaming pipelines from databases, APIs, event streams, and semi-structured data
  • Implement change data capture and incremental loading with support for ordering, deletes, replay, and slowly changing dimensions
  • Create medallion-layer datasets and dimensional data models
  • Deliver data to Snowflake, Microsoft SQL Server, and Databricks
  • Enforce data contracts, reconciliation, and row-level quarantine before publication
  • Manage GitHub workflows and CI/CD, including testing, code review, environment promotion, and infrastructure-as-code deployment
  • Diagnose and resolve production data failures
  • Convert product, valuation, and client-team requirements into reliable operational pipelines

Requirements

  • At least 10 years of experience engineering production data systems
  • Hands-on expertise with Snowflake or Databricks as a primary platform, including data modelling, performance tuning, and cost control
  • Strong Python and SQL skills for developing and testing pipelines
  • Experience with change data capture and event processing, including ordering, replay, and schema evolution
  • Practical Azure experience with Databricks, ADLS, and private network connectivity
  • Experience managing data workloads with GitHub and CI/CD, using GitHub Actions or a comparable system
  • Background in data quality, reconciliation, monitoring, and production incident response
  • Experience creating secure, multi-tenant data platforms with tenant isolation, access controls, and data protection
  • Able to work directly with client technical teams and collaborate with field engineering, product, and other stakeholders
  • Desirable: Experience with Databricks Lakeflow, Auto CDC, Declarative Automation Bundles, and DQX, or Snowflake equivalents such as Dynamic Tables, Streams and Tasks, Snowpark, Snowflake CLI deployments, and Data Metric Functions
  • Desirable: Experience with Debezium, Kafka Connect, or Confluent Kafka
  • Desirable: Experience with Apache Airflow or a comparable workflow orchestrator
  • Desirable: Experience with Kafka or Spark Structured Streaming, Apache Iceberg or Delta Sharing, and dbt
  • Desirable: Experience with private-markets data involving valuations, funds, portfolio companies, or capital activity

Benefits

  • A supportive working environment
  • A culture focused on innovation and collaboration
  • Scope to take ownership and initiative
  • Ongoing opportunities for learning

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