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We are looking for a highly skilled Senior Data Engineer to join our team. In this role, you will play a key part in designing, building and maintaining our data platform and the data-driven systems that power reporting and analytics across the company.

This is a hands-on role where you will contribute to technical decisions, collaborate closely with the team and help improve our data ecosystem. You will work across data engineering and analytics-enablement use cases, helping transform raw operational data into clean, reliable, business-ready data that decision-makers can trust.




Responsibilities




Design, build and maintain scalable and reliable data pipelines and infrastructure on Google Cloud Platform (GCP).
Develop and optimize ETL/ELT processes using Cloud Composer (Airflow) and Dataflow to ingest, transform and load data efficiently (incremental/delta loads via change-tracking/CDC patterns, plus full bulk-refresh patterns).
Model and maintain the data warehouse on BigQuery, authoring and optimizing views, scheduled queries, stored procedures and UDFs across the ingestion, transformation and reporting datasets.
Write production-ready SQL (BigQuery Standard SQL) and Python to support data workflows, transformations and automation.
Own the CI/CD for the warehouse: dataset/table schema-as-code (e.g. Terraform or Dataform), BigQuery deployment pipelines, GitHub Actions workflows, and least-privilege Workload Identity Federation/IAM service accounts for deploys.
Own the reporting-layer publish automation/CI (Python + GitHub Actions) and the BigQuery views/LookML that feed reporting, partnering with the BI Analyst who drives requirements and validates output.
Ensure data quality, accuracy and consistency through validation, monitoring (e.g. Cloud Monitoring/Logging, dbt tests or similar) and testing practices.
Collaborate with engineers, analysts and business stakeholders to translate business requirements into scalable technical solutions.
Participate in code reviews and contribute to technical discussions and improvements.
Support and share knowledge with other engineers in the team.
Stay up to date with new technologies and best practices to continuously improve the data platform.




Technology Requirements

5+ years of professional experience in data engineering or related roles. We are flexible for candidates with strong recent experience in cloud data pipelines and cloud data warehousing.
Strong experience with GCP services, especially BigQuery, Cloud Composer (Airflow)/Dataflow, and Cloud Functions.
Advanced SQL skills (query authoring, stored procedures/UDFs, performance and cost tuning in BigQuery).
Experience with dimensional modeling and building/maintaining a data warehouse.
Production experience writing Python for automation and CI/CD tooling.
Experience with Git and collaborative development workflows, including GitHub Actions.
Familiarity with database DevOps (schema-as-code via Terraform or Dataform, automated BigQuery deployments) and secure cloud auth (IAM, Workload Identity Federation) is a strong plus.
Experience with BI tools — especially Looker (LookML modeling, Looker instance administration) — is a plus.
Awareness of legacy ETL/OLAP technologies is a plus, as some legacy components are still being migrated.




Profile Requirements

Strong problem-solving and technical skills.
Ability to work independently while collaborating effectively with a team.
Comfortable working on complex problems with a hands-on approach.
Good communication skills and ability to work cross-functionally.
Proactive and adaptable in evolving environments.