Job VC
Data Engineer
Technologies
Description
Fit4Me
is looking for a
Data Engineer
whose main goal will be to design, build, and maintain scalable data infrastructure that supports analytics, reporting, and business decision-making across the company.
This role is focused on developing reliable data pipelines, integrating multiple data sources, ensuring data quality, and delivering business-oriented data solutions aligned with product and company goals.
This role is a great fit if you:
Work confidently with SQL (BigQuery), Python, dbt, Google Cloud Platform (GCF, GCS, Cloud Build, Cloud Scheduler, Pub/Sub, IAM), and Data Modeling.
Enjoy working with data at the infrastructure level and want to build a strong foundation for analytics.
Feel comfortable handling large-scale datasets, complex transformations, and multiple data sources at the same time.
Can identify potential issues in data and processes before they affect business results.
Like working closely with analysts and cross-functional teams to build practical and scalable data solutions.
Combine technical depth, systematic thinking, and ownership of results.
Impact Areas:
Building and maintaining ELT processes for collecting, transforming, and loading data from multiple sources into BigQuery.
Developing and scaling the dbt project for data modeling (stage, base, intermediate, dimension, report layers) used for analytics and reporting.
Optimizing pipeline performance, SQL queries, and data models to improve efficiency and reliability.
Validating, testing, and ensuring data quality across all stages of the data pipeline.
Setting up monitoring and alerting for data pipelines and key infrastructure components.
Integrating data from multiple sources (mobile app, finance, marketing), including APIs and external systems.
Working closely with analysts to ensure data is well-structured, easy to use, and supports business needs.
Maintaining up-to-date documentation for data architecture, ELT processes, and dbt models.
Tools you’ll work with: SQL, Python, dbt, and GCP cloud services for building and maintaining data pipelines.
What we expect from you:
3+ years of experience as a Data Engineer or in a related role with a strong focus on pipelines and data modeling.
Deep knowledge of SQL: complex queries, optimization, and understanding of Data Warehousing principles.
Hands-on experience with BigQuery, Snowflake, Redshift, or other analytical data warehouses.
Practical experience with dbt, including testing, documentation, and CI/CD integration.
Experience building and maintaining ELT / ETL pipelines.
Strong Python skills for integrations, automation, and data processes.
Confident use of Git and experience working with GitHub / GitLab.
Experience with GCP or other cloud platforms.
Strong attention to detail, problem-solving mindset, and ownership of results.
Experience working closely with analysts and understanding their data needs.
Nice to have:
Experience optimizing cost efficiency and query performance in cloud environments.
Experience with real-time / streaming data solutions.
Ability to explain technical solutions clearly to business stakeholders.
Hiring process:
Recruiter Interview → Interview with Product Analyst and Data Engineer → Final Interview with HRD → Job Offer 🚀
is looking for a
Data Engineer
whose main goal will be to design, build, and maintain scalable data infrastructure that supports analytics, reporting, and business decision-making across the company.
This role is focused on developing reliable data pipelines, integrating multiple data sources, ensuring data quality, and delivering business-oriented data solutions aligned with product and company goals.
This role is a great fit if you:
Work confidently with SQL (BigQuery), Python, dbt, Google Cloud Platform (GCF, GCS, Cloud Build, Cloud Scheduler, Pub/Sub, IAM), and Data Modeling.
Enjoy working with data at the infrastructure level and want to build a strong foundation for analytics.
Feel comfortable handling large-scale datasets, complex transformations, and multiple data sources at the same time.
Can identify potential issues in data and processes before they affect business results.
Like working closely with analysts and cross-functional teams to build practical and scalable data solutions.
Combine technical depth, systematic thinking, and ownership of results.
Impact Areas:
Building and maintaining ELT processes for collecting, transforming, and loading data from multiple sources into BigQuery.
Developing and scaling the dbt project for data modeling (stage, base, intermediate, dimension, report layers) used for analytics and reporting.
Optimizing pipeline performance, SQL queries, and data models to improve efficiency and reliability.
Validating, testing, and ensuring data quality across all stages of the data pipeline.
Setting up monitoring and alerting for data pipelines and key infrastructure components.
Integrating data from multiple sources (mobile app, finance, marketing), including APIs and external systems.
Working closely with analysts to ensure data is well-structured, easy to use, and supports business needs.
Maintaining up-to-date documentation for data architecture, ELT processes, and dbt models.
Tools you’ll work with: SQL, Python, dbt, and GCP cloud services for building and maintaining data pipelines.
What we expect from you:
3+ years of experience as a Data Engineer or in a related role with a strong focus on pipelines and data modeling.
Deep knowledge of SQL: complex queries, optimization, and understanding of Data Warehousing principles.
Hands-on experience with BigQuery, Snowflake, Redshift, or other analytical data warehouses.
Practical experience with dbt, including testing, documentation, and CI/CD integration.
Experience building and maintaining ELT / ETL pipelines.
Strong Python skills for integrations, automation, and data processes.
Confident use of Git and experience working with GitHub / GitLab.
Experience with GCP or other cloud platforms.
Strong attention to detail, problem-solving mindset, and ownership of results.
Experience working closely with analysts and understanding their data needs.
Nice to have:
Experience optimizing cost efficiency and query performance in cloud environments.
Experience with real-time / streaming data solutions.
Ability to explain technical solutions clearly to business stakeholders.
Hiring process:
Recruiter Interview → Interview with Product Analyst and Data Engineer → Final Interview with HRD → Job Offer 🚀