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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in AWS data platforms, particularly with Amazon Redshift and Glue, focusing on data modeling, performance tuning, and ETL/ELT frameworks. Proven ability to lead teams, mentor engineers, and manage stakeholder relationships in an Agile environment.
Highest-signal resume keywords
AWS Data PlatformsAmazon Redshift Performance TuningAWS Glue Jobs and CatalogAdvanced SQL and Dimensional Data ModellingLeadership and Mentoring
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringETL/ELT FrameworksPython ProgrammingPySparkMySQL Operational Data StoreData-Quality FrameworksDimensional Data ModellingAWS LambdaAWS Step FunctionsAWS IAM
Soft Skills
LeadershipMentoringStakeholder ManagementIndependent WorkAgile Collaboration
Tools & Technologies
AWS GlueAWS CloudWatchGitCI/CD PipelinesLiquibaseDagsterKafkaApache SparkTerraformCloudFormation
Industry Keywords
Data GovernanceFinancial ServicesAI/ML Use CasesData LineageMetadata Practices
Tech Stack
Tools & technologiesAmazon RedshiftApacheAWSETLKafkaMySQLPySparkPythonSparkSQLTableauTerraform
About the role
Key responsibilities & impact- Inventory and baseline Lambda functions, Dagster pipelines, dbt models, MySQL ODS flows, and Kafka feeds
- Document lineage, schedules, SLAs, and failure paths
- Design and build Glue-based ingestion, transformation, and cataloguing patterns using Glue Jobs, Glue Catalog, and Crawlers
- Orchestrate workflows with Lambda, Step Functions, and EventBridge
- Implement S3 Bronze landing with lifecycle and archive policies
- Build Redshift Silver conformance and Gold canonical data products for Customer, Loan, Collections, and Risk
- Build automated data-quality gates, reconciliation checks, and source-to-report lineage
- Support the enterprise data dictionary and certification of business-critical reports
- Implement IAM, AWS-native encryption, Secrets Manager, and Gold-layer RBAC/least-privilege access
- Establish Dev/Test environments, Git-based CI/CD, Liquibase schema version control, and controlled release promotion
- Monitor and recover workloads through CloudWatch
- Migrate and reconcile workloads wave by wave against baselines
- Decommission ODS dependencies and replace reverse flows with governed interfaces
- Optimize Redshift performance and cost using WLM, sort/distribution keys, and materialized views
- Mentor engineers and own technical quality assurance of deliverables
- Work with Data Owners, Champions, and Atos architects in an Agile setup
Requirements
What you’ll need- 10+ years in data engineering
- 5+ years of experience in AWS data platforms
- Deep Amazon Redshift experience: data modelling, performance tuning, WLM, RBAC, external schemas/Spectrum
- AWS Glue jobs, Glue Catalog, Crawlers, and hands-on medallion architecture implementation on S3 and Redshift
- AWS Lambda, Step Functions, S3, IAM, CloudWatch, Secrets Manager
- Advanced SQL and dimensional/canonical data modelling
- ETL/ELT frameworks
- Python as the primary language
- PySpark for Glue jobs
- MySQL operational data store and CDC/batch extraction patterns
- Ability to read, reverse-engineer, and migrate existing dbt models
- Data-quality frameworks, reconciliation, and lineage/metadata practices
- Git, CI/CD pipelines, database schema version control using Liquibase or equivalent, and Dev/Test/Prod promotion
- Leadership, mentoring, stakeholder management, ability to work independently in Agile
- Nice to have: Dagster, Kafka, Apache Iceberg, Apache Spark/EMR, Tableau, Metabase, Terraform/CloudFormation, data-governance tooling, UK financial-services experience, and preparing governed datasets for AI/ML use cases
Benefits
Comp & perks- Diversity and inclusion embedded in Atos's DNA
- Commitment to a fair work environment
- ESG-focused employer
- Opportunities to mentor engineers and work in an Agile setup
