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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining Enterprise Data Warehouses and Business Intelligence Solutions, with a strong focus on ETL/ELT processes, data governance, and BI architecture across cloud and on-premises environments. Proficient in SQL, data modeling, and BI tools to deliver actionable insights and optimize data performance.
Highest-signal resume keywords
Enterprise Data Warehouse DesignETL/ELT Process ImplementationSQL ProficiencyBI Reporting Tools KnowledgeData Governance Practices
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 Warehouse ArchitectureDimensional ModelingETL ToolsSQLData Lake DesignData VirtualizationDatabase Performance TuningData Quality FrameworksData ObservabilityCloud BI Architectures
Tools & Technologies
Power BITableauInformaticaAzure Data FactoryMongoDBKafkaSnowflakeAWS RedshiftCollibraAzure Purview
Certifications & Qualifications
Microsoft Data Analyst AssociateMicrosoft Azure Data Engineer AssociateAmazon Certified Data Analytics Specialty
Industry Keywords
Data LakesData LakehousesBusiness IntelligenceData GovernanceGDPR ComplianceData StewardshipOLAP TechnologiesData Mining ToolsNear Real-Time Data IngestionData Cataloguing
Tech Stack
Tools & technologiesAmazon RedshiftApacheAWSAzureCassandraCloudETLHadoopHBaseInformaticaKafkaMongoDBSQLTableau
About the role
Key responsibilities & impact- Create and maintain Enterprise Data Warehouses and complex Business Intelligence Solutions, including Data Lakes and Data Lakehouses
- Design modern BI architectures for cloud, on-premises, and hybrid environments while ensuring security and data residency compliance
- Gather and analyse business requirements for data pipelines, storage, and reporting solutions
- Design and implement ETL/ELT processes and orchestration workflows with data contracts and schema evolution management
- Implement and manage data virtualization layers for unified access to heterogeneous data sources
- Design and implement dimensional and other data models supporting reporting and analytics
- Design and build dashboards, KPIs, reporting, analytics applications, and self-service BI tools
- Optimize physical database schemas through indexing, partitioning, and storage strategies
- Analyse database performance, tune queries, optimize resource utilization, and meet latency and throughput SLAs
- Implement data quality frameworks, health monitoring, data observability, and SLAs/SLOs
- Define and execute BI solution testing, including data validation, regression tests, contract tests, and CI/CD automation
- Maintain technical documentation such as data dictionaries, lineage, design specifications, and runbooks
- Deploy and configure BI systems using infrastructure-as-code, version control, and environment management
- Govern BI semantic layers, including KPIs, metrics catalogs, naming conventions, and row-level security
- Manage BI access controls and data security in alignment with GDPR and internal data protection policies
- Optimize BI platform and warehouse cost and performance through partitioning, caching, clustering, query optimization, and resource scaling
- Define and enforce data lineage and stewardship practices
- Enable self-service BI through curated datasets, certified reports, and user training/support
Requirements
What you’ll need- Knowledge of enterprise data warehouse design and architecture, including dimensional modelling and star/snowflake schema design
- Knowledge of data lake and lakehouse design patterns, including Delta Lake and Apache Iceberg
- Excellent knowledge of relational database systems applied to data warehouse
- Knowledge of non-relational databases, including MongoDB, Cassandra, and Hadoop HBase
- Excellent knowledge of SQL
- Knowledge of BI reporting and analytics tools, including Power BI, Tableau, and Qlik
- Knowledge of ETL/ELT tools, including Informatica, Talend, dbt, and Azure Data Factory
- Knowledge of modelling tools, including ERwin, SAP PowerDesigner, and ArchiMate
- Knowledge of OLAP technologies and data mining tools, including SSAS, Essbase, SAS Enterprise Miner, and RapidMiner
- Knowledge of near real-time data ingestion and CDC technologies, including Kafka, Debezium, and GoldenGate
- Knowledge of cloud BI architectures, including Azure Synapse, Snowflake, and AWS Redshift
- Knowledge of data governance and cataloguing practices, including Collibra, Alation, and Azure Purview
- Knowledge of data observability practices, including freshness, lineage, and anomaly detection
- English level C1
- Optional: Microsoft Data Analyst Associate, Microsoft Azure Data Engineer Associate, Amazon Certified Data Analytics Specialty, or equivalent certification
Benefits
Comp & perks- 100% Remote opportunities / flexibility to work where you feel most comfortable and productive
- Professional growth and international career opportunities
- Health and Life Insurance
- Tech Visa support for candidates outside the European Union