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We are seeking a skilled Data Engineer with strong expertise in Databricks and Snowflake to design, build, and optimize scalable data pipelines. You will work on high‑performance data processing workflows that support our platform, with a focus on real‑time analytics, large‑scale data transformations, and efficient data modeling.
Job Responsibility:
Build and optimize data pipelines that ingest, validate, and transform core banking data (accounts, transactions, balances, parties, fees) from multiple source systems into our Databricks/Delta Lake lakehouse
Scale and evolve a multi‑tenant architecture, ensuring tenant isolation, efficient partitioning, and consistent schema evolution as we onboard new banks
Own CI/CD for the data platform, including GitHub Actions workflows, SQLMesh plan/apply lifecycle, and Databricks deployment automation
Develop and integrate ML models, including propensity scoring, churn prediction, segmentation, and customer scoring models that feed directly into analytics and decisioning layers
Ensure pipeline reliability through monitoring, alerting, and robust data validation across tenants and environments
Design and maintain 300+ SQL and Python data models across Bronze, Silver, and Gold layers using SQLMesh, with an emphasis on clean abstractions, reusability, and correctness
Own the metrics layer, defining and validating gold‑standard business metrics (revenue, attrition, household analytics, segmentation, balance projections) used by dashboards and APIs
Champion data quality by writing SQLMesh audits, unit tests, and enforcing schema contracts to ensure downstream consumers can trust the data
Collaborate with product and banking domain experts to translate business requirements into well‑modeled, documented, and performant data assets
Drive documentation and discoverability, ensuring data models are self‑describing and easily understood by analysts and product teams
Requirements:
8+ years of software engineering experience, with deep expertise in data engineering and strong exposure to analytics engineering or data modeling
Production experience with SQLMesh or dbt, including building, testing, and deploying transformation projects (SQLMesh strongly preferred)
Hands‑on experience with Databricks or Snowflake, operating pipelines and warehouses in production environments
Advanced SQL skills, including complex window functions, CTEs, incremental logic, and performance‑optimized aggregations
Proficiency in Python, especially for PySpark transformations, data validation, and pipeline automation
Strong understanding of dimensional modeling, medallion/layered architectures, and data quality best practices
Experience with CI/CD for data, including automated testing, version control, and deployment pipelines
Nice to have:
Experience building or operationalizing ML models (propensity, churn, segmentation) within a data platform
Background in banking, financial services, or fintech data domains
Familiarity with Azure services (ADLS Gen2, Azure SQL, Databricks on Azure)
Experience with multi‑tenant SaaS data architectures, including schema isolation and tenant‑aware partitioning
Exposure to data mesh concepts and domain‑oriented data ownership
Familiarity with Databricks Unity Catalog, Auto Loader, or Databricks Workflows
Experience with Linear, GitHub Actions, or similar project management and CI/CD tooling