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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.
विवरण
- 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
आवश्यकताएँ
- 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
लाभ
- 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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