Key skills
Amazon RedshiftAWSAzureBigQueryCloudDistributed SystemsDockerETLGoogle Cloud PlatformHadoopKafkaKubernetesNoSQLPythonSparkSplunkSQLAIMLELTData EngineeringData WarehousingAnalyticsSnowflakeRedshiftGCPGoogle CloudCI/CD
About this role
Role Overview
- Design, build, and maintain scalable data pipelines (ETL/ELT) to support analytics and AI use cases
- Develop and optimise data architectures, data lakes, and data warehouses
- Work closely with stakeholders to translate business needs into data solutions and insights
- Build and maintain data processing scripts and automation tools (Python, SQL)
- Support and enhance AI/analytics platforms and data products
- Implement monitoring, testing, and performance tuning of pipelines
- Collaborate with cross-functional teams (data scientists, engineers, product teams)
Requirements
- 5+ years of experience in Data Engineering / Software Engineering / Data Platforms
- Strong programming skills in Python and SQL
- Experience with ETL/ELT pipelines and data modelling
- Hands-on experience with: Data warehousing (Snowflake, Redshift, BigQuery, etc.)
- Big data tools (Spark, Kafka, Hadoop)
- Experience with cloud platforms (AWS, Azure, or GCP)
- Knowledge of relational and NoSQL databases
- Familiarity with CI/CD and DevOps practices
- Experience with real-time/streaming data pipelines
- Exposure to AI/ML data pipelines or feature engineering
- Experience with containerisation (Docker, Kubernetes)
- Knowledge of data observability and monitoring tools (ELK, Splunk)
- Experience in large-scale distributed systems
Tech Stack
- Amazon Redshift
- AWS
- Azure
- BigQuery
- Cloud
- Distributed Systems
- Docker
- ETL
- Google Cloud Platform
- Hadoop
- Kafka
- Kubernetes
- NoSQL
- Python
- Spark
- Splunk
- SQL
Benefits
- Clear scope with no ambiguity over deliverables.
- Opportunity for repeat engagements based on performance.