Senior Data Engineer

73 Strings

. Redefine the platform’s architecture across ingestion, processing and delivery .

Posted 10/2/2026full-timeLondon • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in data engineering with a focus on building and operating batch and streaming data pipelines, utilizing Snowflake and Databricks for data modeling and performance tuning. Proficient in Python and SQL for pipeline development, with a strong emphasis on data quality, reconciliation, and incident response.

Highest-signal resume keywords

Data EngineeringSnowflakeDatabricksPythonSQL

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentChange Data CaptureEvent ProcessingData Quality MonitoringPerformance TuningIncremental LoadDimensional ModelingBatch ProcessingStreaming ProcessingData Reconciliation

Soft Skills

CollaborationClient InteractionProblem Solving

Tools & Technologies

GitHubCI/CDAzureDatabricks LakeflowApache AirflowKafkaDebeziumConfluent KafkaDbtSnowflake CLI

Industry Keywords

Multi-Tenant Data PlatformsData ProtectionTenant IsolationPrivate Markets DataValuationsPortfolio CompaniesCapital Activity

Tech Stack

Tools & technologies

AirflowApacheAzureKafkaMS SQL ServerPythonSparkSQL

About the role

Key responsibilities & impact

Redefine the platform’s architecture across ingestion, processing and delivery
Build and operate batch and streaming pipelines from databases, APIs, event streams and semi-structured sources
Implement change data capture and incremental load, including ordering, deletes, replay and slowly changing dimensions
Build medallion datasets and dimensional models
Deliver data to Snowflake, Microsoft SQL Server and Databricks
Apply data contracts, reconciliation and row-level quarantine before publication
Own the GitHub workflow and CI/CD, including tests, review, environment promotion and deployment as code
Investigate production data failures
Translate requirements from product, valuation and client-facing teams into operable pipelines

Requirements

What you’ll need

10+ years in data engineering on production systems
Snowflake or Databricks as a primary platform, including modelling, performance tuning and cost management
Python and SQL for pipeline development and testing
Change data capture and event processing, including ordering, replay and schema change
Azure, including Databricks, ADLS and private network connectivity
GitHub and CI/CD for data workloads, using GitHub Actions or an equivalent system
Data quality, reconciliation, monitoring and production incident response
Experience building multi-tenant, secure data platforms, including tenant isolation, access control and data protection
Comfortable working directly with client technical teams, and collaborating across field engineering, product and other stakeholders
Desirable: Databricks Lakeflow, Auto CDC, Declarative Automation Bundles and DQX, or Snowflake equivalents including Dynamic Tables, Streams and Tasks, Snowpark, Snowflake CLI deployments and Data Metric Functions
Desirable: Debezium, Kafka Connect or Confluent Kafka
Desirable: Apache Airflow or equivalent workflow orchestrator
Desirable: Kafka or Spark Structured Streaming, Apache Iceberg or Delta Sharing, and dbt
Desirable: Private markets data experience involving valuations, funds, portfolio companies or capital activity

Benefits

Comp & perks

Supportive environment
Culture of innovation and collaboration
Opportunities to take initiative
Continuous learning