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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 at 73 Strings — Hybrid, India

Lead secure batch and streaming data pipelines for 73 Strings’ private-capital valuation platform. Work across Azure, Databricks, Snowflake, and CI/CD infrastructure.

INIndiaHybridFull-timeSeniorEnglish RequiredData Engineer
AirflowApacheAzureKafkaMS SQL ServerPythonSparkSQL

Description

  • Develop and run pipelines, integrations, and warehouse systems that support valuation and monitoring
  • Take ownership of production data flows from source capture through transformation, reconciliation, and client delivery
  • Manage GitHub-based workflows with automated testing and continuous integration and deployment
  • Contribute to evolving the platform architecture across ingestion, processing, and delivery layers
  • Create and operate batch and streaming pipelines using databases, APIs, event streams, and semi-structured data
  • Implement change data capture and incremental loading with support for ordering, deletions, replay, and slowly changing dimensions
  • Develop medallion architecture datasets and dimensional models in Snowflake, Microsoft SQL Server, and Databricks
  • Enforce data contracts, reconciliation checks, and row-level quarantine before data publication
  • Own data-workload GitHub processes and CI/CD, including testing, code review, environment promotion, and infrastructure-as-code deployment
  • Diagnose production data incidents and convert product, valuation, and client requirements into reliable operational pipelines

Requirements

  • At least 10 years of experience engineering data systems in production
  • Expertise with Snowflake or Databricks as a primary platform, including data modelling, performance tuning, and cost management
  • Proficiency in Python and SQL 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 using GitHub and CI/CD for data workloads through GitHub Actions or an equivalent platform
  • Knowledge of data quality, reconciliation, monitoring, and production incident response
  • Experience building secure, multi-tenant data platforms with tenant isolation, access controls, and data protection
  • Preferred: Databricks Lakeflow, Auto CDC, Declarative Automation Bundles, and DQX, or comparable Snowflake capabilities
  • Preferred: Debezium, Kafka Connect, or Confluent Kafka
  • Preferred: Apache Airflow or a comparable workflow orchestrator
  • Preferred: Kafka or Spark Structured Streaming, Apache Iceberg or Delta Sharing, and dbt
  • Preferred: Experience with private-markets data such as valuations, funds, portfolio companies, or capital activity
  • Ability to work directly with client technical teams and collaborate with field engineering, product, and other stakeholders

Benefits

  • A supportive working environment
  • Room to take initiative and lead improvements
  • Ongoing opportunities for professional learning
  • A collaborative team culture

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