EnablerMinds is a next-generation boutique company delivering end-to-end Cloud, Data & AI solutions.
Our elite team of data engineers, architects, data scientists, AI engineers and industry specialists empowers enterprises to modernize their data ecosystems, accelerate AI adoption, and unlock transformative business value.
With proven methodologies, enterprise-ready frameworks, and an agile delivery and staffing model, we deliver high-performance, scalable, and cost-efficient outcomes tailored to the needs of tomorrow’s intelligent enterprise.
Location: Greece / remote Job Type: Full time About the Role We are looking for a Mid–Senior Data Engineer who will primarily focus on the Gold layer (consumption/use-case layer) of a modern data platform built on Azure and Databricks.
While this is a Data Engineering role, it requires a strong data modelling and analytical mindset, as the position bridges the gap between core data engineering and business-facing analytics.
You will work closely with business stakeholders and analysts to translate use-case requirements into scalable, production-ready data models using established data platform frameworks.
Key Responsibilities Design and implement business-oriented data models in the Gold layer of a Medallion Architecture using existing platform frameworks.
Collaborate closely with Data Analysts and Business Analysts to understand reporting and analytical requirements and translate them into efficient data models.
Leverage existing ingestion, processing, and transformation frameworks (e.g., Databricks, DBT, Azure Data Factory) to deliver new use cases—rather than building foundational pipelines from scratch.
Develop and optimize ETL/ELT transformations using PySpark and Spark SQL within the defined architecture.
Work with curated datasets (Silver layer) to create high-quality, consumption-ready datasets (Gold layer) for reporting and analytics.
Support and, where required, contribute to Power BI (PBI) reporting by ensuring data is modeled appropriately for downstream consumption.
Ensure performance, scalability, and cost-efficiency of transformations and data models in the analytics layer.
Collaborate with central data engineering and platform teams to align on standards, reuse frameworks, and ensure consistency across use cases.
Apply best practices in data governance, security, and quality within the Azure ecosystem.
Required Skills and Qualifications 2–5+ years of experience in data engineering, analytics engineering, or related roles.
Strong hands-on experience with: Azure Data Services (ADLS Gen2, Azure Databricks, ADF, Synapse) Cloud Datawarehouse (Preferably with Databricks SQL or Synapse, Snowflake) SQL (advanced querying, optimization, and data modelling) Python with solid programming fundamentals (OOP concepts) Proven experience in data modelling for analytics/reporting use cases (e.g., star schema, dimensional modelling).
Experience working with large-scale, cloud-based data platforms.
Experience working with different domain data like sales, customer data, finance, ecommerce tracking data, etc.
Familiarity with CI/CD, DevOps, and Infrastructure-as-Code (IaC) practices.
Nice-to-Have Skills Experience with DBT (Data Build Tool) for transformation and modelling.
Know-how of Apache Spark (PySpark, Spark SQL).
Exposure to Power BI or similar BI tools for data consumption.
Knowledge of cloud security and governance best practices.
Relevant certifications in Azure or data engineering.
Contributions to open-source projects in the data ecosystem.
Experience working in ecommerce, sales and marketing domain.
Why Join Us Work with cutting-edge cloud, data and analytics engineering technologies.
Collaborate with a passionate team of data experts, engineers, and architects.
Continuous opportunities for professional growth, certifications, and upskilling.
Be part of innovative projects leveraging modern cloud-native and big data architectures.
This role is ideal for Analytics Engineer who prefers working closer to business use cases rather than core platform/infrastructure development, enjoys data modelling, analytics enablement, and stakeholder interaction and can effectively bridge the gap between data engineering and data analytics.
We welcome applicants of all genders, backgrounds and identities.