Data Engineers

Key Responsibility

You will be assigned a portfolio of client engagements where you will be expected to:

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

Requirements

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:
• Leading end-to-end data platform delivery.
• Architecting enterprise-grade lakehouse environments.
• Implementing data mesh patterns.
• Infrastructure-as-code using tools such as Terraform, Bicep, AWS CDK or Pulumi.
• DevOps and CI/CD pipelines.
• Working effectively with cross-functional teams in a dynamic consulting environment.
• Mentoring junior engineers.
• Contributing to technical strategy and solution direction.

Qualifications

• Bachelor's degree in:
• Computer Science
• Information Systems
• Information Technology
• or a related field.
• Master's degree in a relevant field is advantageous.

Certifications

One or more of the following certifications would be advantageous:

• Microsoft Fabric Data Engineer Associate
• Microsoft Azure Data Engineer Associate
• Databricks Certified Data Engineer Associate
• Google Professional Data Engineer
• AWS Certified Data Engineer – Associate
• Databricks Certified Data Engineer Professional

Technology Experience

Languages & Frameworks

• Python
• PySpark
• SQL
• dbt

Microsoft Fabric & Azure

• Microsoft Fabric Lakehouses
• Fabric Pipelines
• Fabric Semantic Models
• Direct Lake
• Azure Data Factory
• Azure Data Lake Storage Gen2
• Azure Synapse Analytics
• Azure Databricks
• Azure Event Hubs

Google Cloud Platform

• BigQuery
• Cloud Storage
• Dataflow
• Dataproc
• Pub/Sub

Amazon Web Services

• Amazon S3
• AWS Glue
• Amazon Redshift
• Amazon EMR
• Amazon Kinesis

Databricks & Data Platforms

• Databricks
• Delta Lake
• Unity Catalog
• MLflow
• Databricks Workflows

Databases

• Azure SQL
• Azure Cosmos DB
• PostgreSQL
• Snowflake
• BigQuery
• Amazon Redshift

DevOps & Infrastructure as Code

• Git
• Azure DevOps
• GitHub Actions
• Terraform
• Bicep
• AWS CDK
• CI/CD pipelines

Streaming & Messaging

• Azure Event Hubs
• Azure Stream Analytics
• Apache Kafka
• Amazon Kinesis
• Google Pub/Sub

Visualisation & Analytics

• Microsoft Power BI
• Microsoft Fabric Real-Time Dashboards
• Looker / Looker Studio
• Amazon QuickSight