Senior Data Engineer (BQ)

Who we are

You will join the OneMIS stream, responsible for management, regulatory & risk reporting, and advanced analytics. Our mission includes enhancing data quality via KPIs and migrating data platforms to modern, cloud-native ecosystems. We operate in an agile environment, committed to responsible data practices.

We are looking for a Senior Data Engineer to design and deliver scalable data pipelines and high performance analytical solutions using SQL/BigQuery, Spark/PySpark, and Python on Google Cloud. This role focuses on building reliable, cloud native data products that enable advanced reporting, analytics, and decision making across the organization.

What you'll be doing

- Build scalable data pipelines: Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting

- Develop SQL and BigQuery solutions: Write and optimize advanced SQL transformations and build performant, cost‑efficient BigQuery data models

- Develop Python workflows: Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high‑quality code

- Design data models and ensure quality: Build robust data models and apply validation practices to maintain accuracy and reliability

- Build cloud‑native data solutions: Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms

- Optimize performance and reliability: Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance

- Collaborate using strong engineering practices: Work with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards

What You'll Bring Along

- University degree in computer science or a comparable qualification

- At least 5 years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems

- Strong expertise in SQL and hands‑on experience building performant datasets in BigQuery (or similar cloud data warehouses)

- Proven experience with Python and PySpark for scalable data processing in distributed environments

- Solid understanding of data modeling, ELT/ETL patterns, and data quality best practices

- Experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer, GCS, or equivalent cloud data services

- Hands‑on experience building scalable data pipelines (batch and near real‑time) in a cloud‑native environment

- Proficiency with version control, CI/CD pipelines, and automated testing frameworks

- Ability to troubleshoot and optimize performance across compute, storage, and processing layers

Nice to have:

- Experience with Infrastructure as Code (Terraform, Ansible, Chef)

- Knowledge of shell scripting

- Experience in financial services or regulated environments