We are looking for a Senior/Lead Data Engineer to join a major insurance programme and lead the design, development, and optimisation of enterprise data warehouse and pipeline solutions.
This is a deeply hands-on role for someone who is genuinely strong in Python, SQL, and Snowflake, and who can operate at senior level across data engineering design, production pipeline ownership, data quality, performance tuning, and technical leadership. This is not just an execution role. We are looking for someone who can set direction, raise engineering standards, mentor others, and solve complex data platform problems in a live enterprise environment.
WHAT YOU'LL DO
Data Platform Engineering
Lead the design, development, and optimisation of enterprise-grade data pipelines for warehouse, analytics, and downstream reporting use cases
Build and enhance Python-based frameworks for ingestion, transformation, validation, orchestration, and automation
Design and optimise Snowflake-based data structures, semantic layers, and reporting models for scalability, reliability, and cost efficiency
Write and review advanced SQL for transformation, reconciliation, performance tuning, troubleshooting, and large-scale data analysis
Pipeline Ownership & Data Quality
Establish robust ETL/ELT standards, reusable patterns, and engineering best practices across structured and semi-structured data sources
Own data quality, validation, reconciliation, observability, and root cause analysis for complex data issues
Drive reliability, monitoring, alerting, deployment, and production support for critical data pipelines
Support incident response and resolution for high-impact data engineering issues
Technical Leadership
Mentor mid-level and junior engineers through code reviews, design reviews, and knowledge sharing
Lead technical discussions and contribute to design decisions across the data platform
Work closely with business stakeholders, analysts, architects, and engineering teams to translate requirements into robust technical solutions
Champion coding standards, testing, CI/CD, documentation, and disciplined production practices across the team
WHAT WE'RE LOOKING FOR
Must-Have
10+ years of experience in data engineering, analytics engineering, or related data platform roles
Expert-level hands-on Python experience; weak Python is a disqualifier
Advanced SQL skills, including complex transformations, window functions, query optimisation, and performance tuning at scale
Strong experience designing, building, and maintaining large-scale production data pipelines
Deep practical knowledge of Snowflake, including performance tuning, warehouse sizing, clustering, and cost optimisation
Strong understanding of ETL/ELT patterns, batch processing, and incremental / CDC approaches
Experience with structured and semi-structured data such as JSON, Parquet, Avro
Strong ownership mindset around data quality, validation, reconciliation, and observability
Ability to produce clean, maintainable, well-tested, production-ready code
Strong analytical thinking, problem solving, and system design capability
Ability to work Pacific Time
Strong communication and stakeholder management skills
Ability to mentor engineers and lead technical/code/design discussions
Comfortable with a live coding assessment in Python and SQL
Preferred
DBT
Azure services such as Data Factory, ADLS, Functions, DevOps
Experience with orchestration tools such as Airflow, Prefect, Dagster, or similar
Experience with Salesforce data models or Salesforce integration patterns
Experience in insurance, financial services, or other regulated environments
Exposure to data governance, lineage, metadata, CI/CD for data, Terraform, or broader DataOps practices
WHAT SETS YOU APART
You are not just a pipeline builder, you can shape engineering patterns and raise the bar for the team
You combine deep Snowflake + Python + SQL capability with real production ownership
You can solve data trust, quality, and reconciliation problems, not just move data between systems
You are senior enough to lead technically, while still staying genuinely hands-on