🚀 Senior Data Engineer – Mindera
At Mindera, we build high-performing, scalable technology solutions that help businesses innovate and grow. We bring together engineering expertise, collaboration, and a passion for technology to deliver impactful solutions.
We are looking for an experienced Data Engineer with strong hands-on expertise in Snowflake, dbt, SQL, Python, and AWS data services. The ideal candidate will have prior experience in Analytics Engineering, with the ability to maintain, optimise, and enhance existing data models and pipelines while contributing to the team's data engineering initiatives.
Role Overview
As a Data Engineer, you will be responsible for designing, developing, maintaining, and optimising data pipelines and analytical data models. You will work extensively with Snowflake, dbt Core/dbt Cloud, SQL, Python, and AWS services to ensure reliable, scalable, and high-quality data transformation workflows.
The ideal candidate will bring strong Analytics Engineering experience and be capable of taking ownership of existing data solutions with minimal ramp-up time.
Requirements
Key Responsibilities
Design, develop, and maintain scalable data pipelines and transformation workflows.
Build and optimise data models using Snowflake to support analytics and reporting requirements.
Manage Snowflake virtual warehouses, optimise queries, and improve performance and compute efficiency.
Develop and maintain dbt models using dbt Core and dbt Cloud, including incremental models, macros, and automated testing.
Write complex, efficient SQL queries for data transformation, validation, and processing.
Develop Python scripts for data engineering, pipeline automation, and workflow management.
Work with Snowflake stored procedures and Python-based data processing solutions.
Build, maintain, and troubleshoot Apache Airflow DAGs for data pipeline orchestration.
Implement and support data ingestion and processing workflows using AWS services, including S3, Glue, and Kinesis Data Firehose.
Design and implement dimensional data models, including fact tables, dimension tables, and star/snowflake schemas.
Monitor data pipelines, troubleshoot production issues, and ensure data quality, consistency, and reliability.
Collaborate with analytics engineers, data engineers, analysts, and other stakeholders to translate business requirements into effective data solutions.
Follow best practices for version control, testing, documentation, deployment, and maintainability.
Take ownership of existing data workflows and contribute to continuous improvements in pipeline performance and data modelling.
Required Technical Skills
Snowflake
Strong hands-on experience with Snowflake data warehousing.
Experience with warehouse management, query optimisation, and performance tuning.
Knowledge of Snowflake architecture, compute management, and data modelling.
dbt Core and dbt Cloud
Strong experience developing and maintaining dbt projects.
Hands-on expertise in incremental models, macros, and dbt testing.
Understanding of model dependencies, documentation, and deployment workflows.
SQL
Advanced SQL skills, including complex joins, CTEs, window functions, and aggregations.
Experience optimising queries and processing large datasets.
Python
Experience developing Python scripts for data engineering and pipeline automation.
Familiarity with Snowflake stored procedures and Python-based integrations.
Experience building or maintaining Airflow DAGs.
AWS and Data Pipeline Orchestration
Hands-on experience with AWS data services, including:
Amazon S3
AWS Glue
Amazon Kinesis Data Firehose
Experience using Apache Airflow for pipeline orchestration, scheduling, and monitoring.
Ability to troubleshoot and maintain production data pipelines.
Dimensional Modelling
Strong understanding of dimensional modelling principles.
Experience designing fact and dimension tables.
Knowledge of star and snowflake schemas and analytical data modelling practices.
Experience and…