Company Profile
Flentas is an AI, Data and cloud-first consulting and engineering company specializing in building, modernizing, and managing AI, Data and applications on AWS and other modern cloud platforms.
With strong delivery capabilities across Data, AI, Cloud, DevOps, and managed services, Flentas is now focused on driving the next phase of growth through solution-led offerings and ecosystem-driven scale.
About the Role
We are looking for a skilled Databricks Engineer to join our Data & AI practice. You will design, build, and optimize data pipelines and lakehouse solutions on the Databricks platform, working closely with data scientists, analysts, and cloud architects to deliver scalable, production-grade data products.
Key Responsibilities
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Design and implement data pipelines using Databricks, Apache Spark, and Delta Lake
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Build and maintain Lakehouse architectures (Bronze / Silver / Gold layers)
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Develop and optimize ETL/ELT workflows using PySpark, SQL, and Databricks notebooks
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Collaborate with data scientists to productionize ML models using MLflow and Databricks ML Runtime
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Implement data quality frameworks, monitoring, and observability practices
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Manage Databricks clusters, job scheduling, and cost optimization
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Integrate Databricks with cloud platforms (AWS / GCP / Azure) and data sources (Kafka, ADLS, S3, BigQuery)
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Enforce data governance, security, and access control using Unity Catalog
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Participate in code reviews, technical design discussions, and documentation
Required Skills & Experience
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4–8 years of overall data engineering experience, with at least 2 years hands-on with Databricks
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Strong proficiency in PySpark, Spark SQL, and Python
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Experience with Delta Lake — schema evolution, ACID transactions, time travel
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Hands-on with Databricks Workflows, Auto Loader, and DLT (Delta Live Tables)
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Solid understanding of Lakehouse design patterns and medallion architecture
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Experience integrating Databricks with at least one major cloud (AWS / GCP / Azure)
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Familiarity with CI/CD for data pipelines (Git, Databricks Asset Bundles or similar)
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Knowledge of data modeling — dimensional, Data Vault, or OBT patterns
Good to Have
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Databricks Certified Associate or Professional certification
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Experience with Unity Catalog and data governance at scale
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Exposure to MLflow, Feature Store, or Model Serving
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Experience with dbt on Databricks
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Knowledge of streaming use cases (Structured Streaming, Kafka integration)
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Prior experience in a consulting or delivery environment
Soft Skills
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Strong problem-solving and analytical thinking
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Ability to work independently and communicate effectively with cross-functional stakeholders
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Comfortable handling ambiguity and shifting priorities in a delivery-focused environment
