The ad says“Hands-on experience with cloud platforms (AWS, Azure, GCP)”
The ad says“Hands-on experience with cloud platforms (AWS, Azure, GCP)”
The ad says“Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines”
The ad says“Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines”
The ad says“Understanding of data governance, security, and compliance frameworks”
The ad says“Data Modelling & Optimisation (Delta Lake, Medallion architecture)”
The ad says“Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon)”
The ad says“Familiarity with Databricks Workflows and other orchestration tools”
The ad says“Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent”
The ad says“Data Modelling & Optimisation (Delta Lake, Medallion architecture)”
The ad says“As a trusted practitioner, you will design and implement robust ETL/ELT workflows, integrate real-time and batch data sources, and enable secure, well-governed data products and pipelines.”
The ad says“Hands-on experience with cloud platforms (AWS, Azure, GCP)”
The ad says“Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines”
The ad says“Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon)”
The ad says“Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon)”
The ad says“Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent”
The ad says“Data Modelling & Optimisation (Delta Lake, Medallion architecture)”
The ad says“Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow)”
The ad says“Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow)”
The ad says“Strong SQL and Python (or equivalent language) skills for data manipulation and automation”
The ad says“Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow)”
The ad says“Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow)”
The ad says“Strong SQL and Python (or equivalent language) skills for data manipulation and automation”
The ad says“Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon)”
The ad says“Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent”
The ad says“Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow)”
Values
The ad says“Databricks certifications (Data Engineer Professional) highly desirable”
We are seeking a highly skilled Databricks Data Engineer to join our Data & AI practice.
The successful candidate will have deep expertise in building scalable data pipelines, optimizing Lakehouse architectures and enabling advanced analytics and AI use cases on the Databricks platform.
This role is critical in building and optimizing modern data ecosystems that enable data-driven decision making, advanced analytics, and AI capabilities for our clients
As a trusted practitioner, you will design and implement robust ETL/ELT workflows, integrate real-time and batch data sources, and enable secure, well-governed data products and pipelines.
You will thrive in a collaborative, client-facing environment, with a passion for solving complex data challenges, driving innovation and ensuring the seamless delivery of data solutions
Primary Responsibilities;
Client Engagement & Delivery
Data Pipeline Development (Batch and Streaming)
Databricks & Lakehouse Architectures
Data Modelling & Optimisation (Delta Lake, Medallion architecture)
Collaboration & Best Practices
Quality, Governance & Security
Business Relationships;
Solution Architects
Data Engineers, Developers, ML Engineers, and Analysts
Client stakeholders up to Head of Data Engineering, Chief Data Architect, and Analytics leadership
Measures of Success;
Delivery of high-performing, scalable, and secure data pipelines aligned to client requirements
High client satisfaction and successful adoption of Databricks-based solutions
Demonstrated ability to innovate and improve data engineering practices
Contribution to the growth of the practice through reusable assets, accelerators, and technical leadership
Preferred experience
Strong consulting values with ability to collaborate effectively in client-facing environments
Strong problem-solving, analytical, and communication skills
Hands-on expertise across the data lifecycle: ingestion, transformation, modelling, governance, and consumption
Experience leading or mentoring teams of engineers to deliver high-quality scalable data solutions
Proven experience in data engineering and pipeline development on Databricks and cloud-native platforms
Understanding of data governance, security, and compliance frameworks
Proficiency in ETL/ELT tools such as DBT, Matillion, Talend, or equivalent
Hands-on experience with cloud platforms (AWS, Azure, GCP)
Strong SQL and Python (or equivalent language) skills for data manipulation and automation
Familiarity with Databricks Workflows and other orchestration tools
Deep expertise with the Databricks platform (Spark/PySpark/Scala, Delta Lake, Unity Catalog, MLflow)
Experience with version control tools (GitHub, Bitbucket) and CI/CD pipelines
Exposure to AI/ML workloads desirable
Familiarity with medallion architectures, data lakehouse principles and distributed data processing
Knowledge of data modelling methodologies (star schemas, Data Vault, Kimball, Inmon)
Experience: Minimum 5–8 years in data engineering, data warehousing, or data architecture roles, with at least 3+ years working with Databricks
Preferred: BSc/MSc in Computer Science, Data Engineering, or related field
