Senior Data Engineer-1

Data Ingestion & Integration: Design and build pipelines that reliably move data from a wide variety of source systems – relational databases, external sources and operational datastores - into our Lakehouse, merging disparate sources into a single, trustworthy picture for the business.

Transformation & Modeling: Build and maintain ELT pipelines using dbt to transform raw data into clean, well-modeled, business-ready assets, applying dimensional modeling and consistent KPI definitions so the same metric never means two different things in two different dashboards.

Platform Contribution: Operate and extend our Azure Databricks Lakehouse, contributing to schema design, query optimization, and scalable data processing so the platform scales reliably as data volume and complexity grow.

Orchestration & Reliability: Own end-to-end pipeline orchestration in Airflow, building workflows that run in the right order, recover automatically from failure, and are observable through monitoring, alerting, and audit trails.

Data Quality & Trust: Implement automated data quality checks and testing so that accuracy, completeness, and timeliness are verified continuously, not discovered by a business user staring at a broken dashboard.

Data Mesh & Domain Ownership: Apply data mesh principles to design data products with clear ownership, discoverability, and contracts across domains, reducing tight coupling between teams and enabling other domains to self-serve trusted data.

Automation & Engineering Rigor: Bring a software engineering mindset to Data Engineering - version-controlled code, CI/CD, code review, and Infrastructure as Code - so pipelines are tested, repeatable, and maintainable rather than one-off scripts.

Cross-Functional Partnership: Work directly with business stakeholders to translate ambiguous requirements into scalable data solutions and collaborate closely with the Platform and AI teams to align on shared infrastructure, standards, and roadmap, communicating trade-offs clearly to both technical and non-technical audiences.

Cost & Performance Ownership: Monitor and optimize computing and storage costs on Azure, treating cost efficiency as a first-class engineering concern rather than an afterthought.

• AI-Assisted Engineering: Use AI pair-programming tools such as GitHub Copilot to accelerate development and build agentic CI/CD workflows in GitHub Actions that automate testing, review, and deployment with minimal manual intervention.