Key skills
PythonSQLGoogle BigQueryCloud ComposerApache AirflowVertex AI PipelinesTableauData Architecture Patterns - MedallionData Architecture Patterns - Star SchemaData Architecture Patterns - Data VaultTerraformGoogle Cloud PlatformGitGitLabAIMLData EngineeringAnalyticsBigQueryGCPVaultCloud FunctionsGCSVertex AICI/CD
About this role
Mindlance is a confidential company looking for a Senior Data Engineer & Analytics Developer who can manage the full data lifecycle from pipeline development to dashboard creation. The role requires strong analytics capabilities, proficiency in Python and SQL, and experience with Google BigQuery and Tableau to create efficient, reusable data solutions.
Responsibilities:
- Own the full data lifecycle — from scalable pipeline development to polished Tableau dashboards
- Design datasets for reuse and longevity, resisting the urge to create one-off tables for every new request
- Build and maintain Vertex AI Pipelines for ML workflows and data transformation at scale
- Write complex, performant, and maintainable SQL queries across large datasets
- Design layered data architectures using patterns such as Medallion, Dimensional Modeling, and Data Vault
- Build modular, multi-purpose datasets rather than project-specific tables
- Apply best practices around naming conventions, schema organization, documentation, and lifecycle management
- Build production-quality Tableau dashboards and translate business questions into clear visualizations
- Understand the relationship between upstream data modeling decisions and downstream dashboard performance
Requirements:
- 5+ years in a data engineering role, with meaningful GCP/BigQuery experience
- Advanced proficiency in Python and SQL as daily working languages
- Demonstrated experience designing and maintaining shared, reusable data models in an enterprise or multi-team environment
- Familiarity with data architecture patterns including Medallion, star schema, and Data Vault
- Portfolio or examples of Tableau dashboards built on well-structured data layers
- Familiarity with CI/CD practices for data pipelines and infrastructure-as-code concepts
- Strong communicator who can work with cross-functional teams to gather requirements and translate them into scalable data solutions
- Infrastructure: Terraform (preferred), GCS, Cloud Functions