We are hiring a Lead Data Engineer to drive the development of a modern data platform for an insurance client.
This role combines technical leadership and hands-on engineering, focusing on building
scalable data pipelines and enabling analytics using Snowflake, dbt, and Fivetran.
The ideal candidate thrives in greenfield environments, can handle complex source systems, and enforces engineering discipline without over-engineering.
a) Lead end-to-end delivery of data engineering initiatives, ensuring alignment with business goals and timelines
b) Design and implement scalable ingestion frameworks across APIs, files, databases, and SaaS systems (including Fivetran SDK), handling schema drift, incremental loads, and historical backfills
c) Collaborate with stakeholders across business, analytics, and IT teams to define requirements and success metrics
d) Build and maintain dbt workflows (models, tests, macros, snapshots) with a focus on staging and scalable transformation design
e) Integrate data from Guidewire, Mainframe, APIs, and other enterprise systems
f) Implement orchestration workflows using Prefect or equivalent tools
g) Set up CI/CD pipelines using GitHub Actions and manage deployments via dbt Cloud
h) Ensure data quality, reliability, and efficient pipeline performance across ingestion and transformation layers
Mentor team members and drive engineering best practices
i) Implement logging, metrics, monitoring, and alerting frameworks to ensure pipeline reliability
j) Proactively detect and resolve failures, performance bottlenecks, and data quality issues
k) Apply agile principles to iterate quickly, adapt to change, and contribute to continuous improvement
l) Implement data organization, partitioning, and lifecycle policies (archival, retention, cost control) to support efficient ingestion and historical backfills
m) Contribute to the evolution of the data platform, supporting future expansion into curated and analytics-ready data layers
a) Strong hands-on experience in Snowflake, DBT (Core/Cloud), and Fivetran (including SDK/custom connectors preferred)
b) Proficiency in SQL and Python
c) Build and maintain transformation workflows using DBT (models, tests, macros, snapshots)
d) Optimize Snowflake performance (query tuning, clustering, cost optimization)
e) Solid experience in workflow orchestration (Prefect preferred; Airflow acceptable)
f) Hands-on experience with CI/CD pipelines (GitHub Actions or similar)
g) Exposure to AWS Glue, insurance domain or enterprise data systems is a plus
h) Exposure on AI Driven documentation using data dictionaries and source docs to populate dbt
descriptions for objects & columns
i) Familiarity with APIs, semi-structured data (JSON/XML), and data lakes
j) Solid understanding of data warehouse concepts, dimensional modeling, and metadata management.
a) 6–10 years in Data Engineering
b) Prior experience in a Lead or Senior role preferred
c) Experience with Cloud platforms (Azure, AWS, or GCP).
d) Exposure to monitoring/observability tools (Datadog, CloudWatch, Snowflake Query History, etc.).
e) Background in analytics, BI tools, or data governance.
f) Design and manage raw data landing zones in Amazon S3 to support file-based ingestion and archival strategies