About the Role:
We are seeking a highly skilled and experienced Data Engineer to join our dynamic data and analytics team. The ideal candidate will have strong technical skills in AWS data services and will be responsible for designing, developing, and implementing robust, insightful, data-intensive solutions on AWS. This role requires a deep understanding of data engineering, strong SQL skills, and extensive hands-on experience with services such as Amazon Redshift, Amazon Athena, AWS Glue, Amazon EMR, Amazon Kinesis, AWS DMS, Amazon S3, and AWS orchestration services for data pipelines. You will play a crucial role in building an AWS-native cloud data platform.
Responsibilities:
Design, build, and maintain scalable batch and streaming data pipelines using AWS Glue, Amazon EMR, Amazon Kinesis, and AWS Lambda.
Model and optimise analytical data stores in Amazon Redshift and S3-based data lakes, including partitioning, file-format, and query-performance tuning.
Implement CDC and ingestion patterns from operational databases and SaaS sources using AWS DMS, Glue connectors, and Kinesis.
Orchestrate and monitor pipeline workflows using Amazon MWAA (Managed Workflows for Apache Airflow), AWS Step Functions, and Amazon EventBridge.
Own data cataloguing, lineage, and fine-grained access control via AWS Glue Data Catalog and AWS Lake Formation.
Contribute to the development, deployment, and lifecycle management of data applications on AWS, leveraging services including EC2, Amazon EKS, AWS Lambda, AWS Fargate, Amazon RDS/Aurora, DynamoDB, and Amazon S3.
Embed data quality, observability, cost optimisation, and security controls (encryption, IAM least privilege, PII handling) into every pipeline.
Collaborate with analysts, data scientists, and business stakeholders to translate requirements into production-grade data products.
Required Skills and Qualifications:
Bachelor's degree in Computer Science, Information Technology, Data Analytics, or a related field.
5–8 years of overall IT experience, with 2–3 years of hands-on experience designing and developing data applications on AWS.
Deep expertise in AWS services and architectures, including but not limited to:
Compute: EC2, Amazon EKS, Amazon ECS, AWS Lambda, AWS Fargate, AWS Batch.
Storage & Databases: Amazon S3, Amazon RDS, Amazon Aurora, Amazon Redshift, DynamoDB, Amazon Keyspaces, Amazon ElastiCache.
Data & Analytics: AWS Glue (ETL, Data Catalog, DataBrew), Amazon EMR, Amazon Athena, Amazon Kinesis (Data Streams, Firehose, Managed Service for Apache Flink), Amazon MSK, AWS DMS, AWS Lake Formation, Amazon MWAA, AWS Step Functions, Amazon QuickSight.
Operations & Monitoring: Amazon CloudWatch, AWS CloudTrail, AWS X-Ray, AWS Cost Explorer.
Strong SQL skills, including performance tuning on large datasets.
Proven ability to translate business requirements into technical solutions.
Excellent analytical, problem-solving, and critical-thinking skills.
Strong communication and interpersonal skills, with the ability to collaborate effectively with technical and non-technical stakeholders.
Experience working in an Agile development methodology.
Ability to work independently, manage multiple priorities, and meet tight deadlines.
Preferred Skills (Nice to Have):
AWS Certified Data Engineer – Associate, or AWS Certified Solutions Architect certification.
Proficiency in Python or PySpark for data manipulation and automation.
Experience with infrastructure as code (Terraform, AWS CDK, or CloudFormation) and CI/CD for data pipelines.
Experience with another hyperscaler (GCP or Azure), demonstrating breadth in data engineering.
Exposure to modern data lakehouse table formats such as Apache Iceberg, Delta Lake, or Apache Hudi.