Job VC
Data Engineer (Health and Fitness app)
Technologies
Description
We are looking for a
Data Engineer
to join our partner team.
The primary goal of this role is to design, build, and maintain a scalable data infrastructure that supports analytics, reporting, and data-driven decision-making across the organisation.
This role focuses on developing reliable data pipelines, integrating multiple data sources, ensuring data quality, and delivering business-oriented data solutions aligned with company goals.
What you will do:
Development and maintenance of ELT processes for collecting, transforming, and loading data from various sources into BigQuery.
Creation and development of a dbt project to build data models (stage, base, intermediate, dimension, report layers) used in analytics and reporting.
Optimization of performance of pipelines, SQL queries, and data models.
Validation, testing, and data quality control at all stages of the pipeline.
Setting up monitoring and alerting for data pipelines and key infrastructure components.
Integration of data from various sources (mobile application, financial and marketing information), including APIs and external systems.
Collaboration with analysts to ensure data is properly structured, easy to use, and supports the team’s analytical needs.
Maintaining up-to-date documentation regarding data architecture, ELT processes, and dbt models.
Tools you will work with: SQL, Python, dbt, and GCP cloud services for building and maintaining data pipelines.
What we expect from you:
3+ years of experience in a Data Engineer position or a related role with a strong focus on building pipelines and data modeling.
Deep knowledge of SQL: complex queries, optimization, understanding of Data Warehousing principles.
Practical experience working with BigQuery, Snowflake, Redshift, or other analytical warehouses.
Experience working with dbt, including testing, documentation, and CI/CD integration.
Experience building and maintaining ELT/ETL pipelines.
Strong proficiency in Python for integrations, automation, and data processes.
Confident use of Git and experience working with GitHub/GitLab.
Experience working with GCP or other cloud platforms.
Attention to detail, strong problem-solving mindset, and ownership of results.
Experience collaborating with analysts and understanding their data needs.
Nice to have:
Experience optimizing cost efficiency and query performance in cloud environments.
Experience working with real-time / streaming data solutions.
Ability to clearly explain technical solutions to stakeholders.
Data Engineer
to join our partner team.
The primary goal of this role is to design, build, and maintain a scalable data infrastructure that supports analytics, reporting, and data-driven decision-making across the organisation.
This role focuses on developing reliable data pipelines, integrating multiple data sources, ensuring data quality, and delivering business-oriented data solutions aligned with company goals.
What you will do:
Development and maintenance of ELT processes for collecting, transforming, and loading data from various sources into BigQuery.
Creation and development of a dbt project to build data models (stage, base, intermediate, dimension, report layers) used in analytics and reporting.
Optimization of performance of pipelines, SQL queries, and data models.
Validation, testing, and data quality control at all stages of the pipeline.
Setting up monitoring and alerting for data pipelines and key infrastructure components.
Integration of data from various sources (mobile application, financial and marketing information), including APIs and external systems.
Collaboration with analysts to ensure data is properly structured, easy to use, and supports the team’s analytical needs.
Maintaining up-to-date documentation regarding data architecture, ELT processes, and dbt models.
Tools you will work with: SQL, Python, dbt, and GCP cloud services for building and maintaining data pipelines.
What we expect from you:
3+ years of experience in a Data Engineer position or a related role with a strong focus on building pipelines and data modeling.
Deep knowledge of SQL: complex queries, optimization, understanding of Data Warehousing principles.
Practical experience working with BigQuery, Snowflake, Redshift, or other analytical warehouses.
Experience working with dbt, including testing, documentation, and CI/CD integration.
Experience building and maintaining ELT/ETL pipelines.
Strong proficiency in Python for integrations, automation, and data processes.
Confident use of Git and experience working with GitHub/GitLab.
Experience working with GCP or other cloud platforms.
Attention to detail, strong problem-solving mindset, and ownership of results.
Experience collaborating with analysts and understanding their data needs.
Nice to have:
Experience optimizing cost efficiency and query performance in cloud environments.
Experience working with real-time / streaming data solutions.
Ability to clearly explain technical solutions to stakeholders.