Major responsibilities:

— Design, develop and optimize scalable ETL processes and data pipelines

— Develop and maintain BI solutions, data marts and analytical datasets

— Design and optimize complex SQL scripts, procedures, and data processing workflows

— Manage risks and dependencies by identifying technical and delivery threats such as data quality, legacy alignment and capacity, communicating them to stakeholders, and proposing pragmatic mitigations early

— Align stakeholders by defining and agreeing on approaches and trade‑offs, presenting options and recommendations, and running demos to validate progress and demonstrate value

— Provide technical leadership with hands‑on delivery by reviewing and enforcing architecture, designs, code and SQL/notebooks, and by implementing critical components to ensure consistent quality.

We'd love to hear from you if you have:

— Minimum of 5 years of experience in data engineering, with at least 2–3 years focused on the Azure cloud ecosystem

— Expert in SQL with proven ability to write and optimize complex analytical queries, stored procedures and functions

— Deep knowledge of PySpark and Python for large-scale data processing and building ETL/ELT pipelines

— Understanding data organization principles within a Data Lake (Raw, Silver, Gold layers)

— Experience in administration and development within managed instance environments

— Knowledge of data modeling methodologies (Kimball/Inmon), understanding of Slowly Changing Dimensions (SCD), and history management

— Deep understanding of enterprise Data Warehouse architecture, including the design of dimension and fact tables, and the creation of aggregated data layers

— Financial/Insurance Data Experience, understanding of month-end close processes, data reconciliation, and financial calculation logic

— Experience in performing code reviews, designing pipeline architecture, and overseeing the technical quality of the team's output

— Experience working in Scrum teams, with the ability to decompose high-level business goals into specific technical tasks (User Stories/Tasks) and manage the delivery plan

— English level B2 or higher.

Nice to have:

— Experince orchestrating complex task chains in Azure Data Factory

— Knowledge of the insurance domain including premiums, commissions, premium taxes, and actuarial calculations

— Experience with BI tools such as Power BI and understanding how end users consume data from a DWH

— Practical experience performing lift-and-shift migrations of logic from legacy databases to a cloud-based warehouse

— Azure certification preferred, for example Microsoft Certified: Azure Data Engineer Associate (DP-203).