Design and build scalable data platforms using modern cloud-native and Lakehouse architectures
Develop and optimise data pipelines using Python, SQL, and tools such as Azure Data Factory, AWS Glue, Google Cloud Dataflow, Databricks, and dbt
Modernise legacy data environments, migrating from on-premises solutions to cloud-native platforms such as Microsoft Fabric, Azure Synapse Analytics, AWS Redshift, Google BigQuery, or Databricks
Engage with clients to conceptualize data solutions aligned to their business strategy
Support our sales team with pre-sales activities, proof-of-concept deliveries, and technical proposals
Provide technical guidance and mentorship to junior and intermediate consultants
Lead technical reviews and contribute to consultants' growth plans
Identify opportunities to automate manual processes, optimise data delivery, and improve infrastructure scalability
Work with stakeholders, including executive, product, and analytics teams, to address data infrastructure needs
Drive knowledge sharing through technical blogs, internal forums, and workshops
Balance billable project work with team support responsibilities
Data Engineer Candidate Requirements
Intermediate Level
3-5 years' experience
3-5 years of hands-on experience in data engineering.
Strong proficiency in Python and/or SQL, including query optimisation.
Experience working with both relational and non-relational databases.
Experience designing and building data pipelines and data models.
Understanding and practical experience with lakehouse architectures, including the medallion pattern.
Practical experience with at least one major cloud platform, including:
Microsoft Azure
AWS
Google Cloud Platform (GCP)
Familiarity with:
Databricks
Snowflake
Delta Lake
PySpark
Understanding of data transformation frameworks such as dbt.
Experience with version control using Git.
Understanding of CI/CD practices for data workflows.
Strong analytical and problem-solving skills.
Ability to perform root-cause analysis on complex data issues.
Good communication and stakeholder engagement skills.
Senior Level
6-8+ years' experience
6-8+ years of hands-on experience in data engineering.
All intermediate-level technical requirements, together with demonstrable experience in: