Senior Hybrid Data Platform

As a Senior Hybrid Data Platform & Data Warehouse Engineer, you are responsible for designing, operating and continuously improving TP's data warehouse, ODS integration and data platform capabilities across a hybrid data landscape. Azure is the strategic standard for the main data warehouse, using Azure SQL / SQL Server, Azure Data Factory, Azure DevOps, staged data pipelines, business rules, curated data marts and BI/AI-ready datasets. At the same time, part of the operational data store remains hosted on Oracle / Oracle Cloud, with platform management partly outsourced to an external supplier. The role therefore requires strong Azure data warehouse capability, combined with practical understanding of Oracle ODS, PostgreSQL, external ETL tooling, scheduling, APIs and cross-platform data integration. You ensure that operations, account reporting, BI dashboards and AI-enabled features are supported by data that is trustworthy, timely, secure, well-modelled and available. You prepare, monitor and improve data flows across multiple stages: ingestion, raw/landing, staging, cleansing, business rules, curated datasets, data marts and reporting or analytics consumption layers. You work with Azure Data Factory, SQL, Azure DevOps and data warehouse structures, while also understanding integrations with Oracle ODS, Talend, Chronicle scheduling, Databricks and future BI environments such as Power BI, Google Looker and Looker Studio. You treat data services as production services. This means applying ITIL incident, problem, change and release practices; monitoring freshness and data quality; maintaining metadata and lineage; implementing access controls; coordinating with external suppliers where needed; and documenting pipelines, business rules, tables, dependencies, runbooks and known limitations. The role works closely with BI / Data Services, IT Engineering & Development, Business Service & AI Demand Partners, external data platform suppliers and operational stakeholders.

Key Accountabilities

Azure data warehouse: Design, operate and improve the Azure-hosted data warehouse using Azure SQL / SQL Server and related Azure data services.

Azure Data Factory: Build, maintain and monitor ADF pipelines for ingestion, transformation, orchestration, scheduling and error handling.

Hybrid ODS integration: Work with Oracle / Oracle Cloud ODS data sources and coordinate with external Oracle management suppliers where required.

ETL tooling: Develop, support or troubleshoot ETL processes across Azure Data Factory, Talend and other ETL components.

Scheduling: Understand and support external scheduling / orchestration mechanisms such as Chronicle open source scheduler where used.

API integration: Support API-based ingestion and integration patterns across operational, reporting and data platform environments.

Data warehouse stages: Manage data through raw/landing, staging, cleansing, transformation, business rules, curated data and data mart layers.

Business rules: Implement, document and maintain business rules, mappings, transformations, calculations and validation logic.

Data marts: Design and maintain domain-specific data marts for operations, accounts, service management, CX, workforce or reporting consumption.

BI enablement: Support reliable consumption by Power BI and future Google BI tooling such as Looker / Looker Studio.

Databricks alignment: Support integration or data exchange with global Databricks environments where applicable.

PostgreSQL: Support or understand PostgreSQL-based data sources or services where they are part of the integration landscape.

Azure DevOps: Use Azure DevOps for backlog, repositories, pull requests, versioning, deployment tracking and controlled release across environments.

Data quality: Continuously measure completeness, accuracy, consistency, duplication, latency and reconciliation quality on critical datasets.

Metadata and lineage: Maintain metadata, lineage, data ownership, safe-use classification and known limitations.

ITIL discipline: Apply incident, problem, change and release practices to data warehouse, ETL and data platform services.

Supplier coordination: Coordinate technically with external suppliers managing Oracle Cloud or other data platform components.

Security and compliance: Implement least privilege, access reviews, audit logging, data classification and privacy controls.

AI data readiness: Ensure critical datasets are classified, documented, quality-scored and safe to use for AI-enabled use cases.

Qualification & Requirements

Minimum 4–6 years of experience in data engineering, data warehouse engineering, database development, ETL development or data platform operations.

Strong hands-on experience with Azure SQL / SQL Server and SQL-based data warehouse development.

Strong experience with Azure Data Factory, including pipeline design, orchestration, monitoring, triggers, dependencies, parameterization and error handling.

Experience designing or maintaining staged data warehouse architectures, including raw/landing, staging, cleansing, business rules, curated and data mart layers.

Strong understanding of dimensional modelling, fact/dimension design, star/snowflake schemas and business-ready data marts.

Experience implementing business rules, mappings, calculations and transformation logic in SQL and/or ETL tooling.

Experience using Azure DevOps for work tracking, version control, pull requests, release tracking or controlled deployment management.

Experience with Power BI consumption patterns, dataset refresh dependencies, semantic models or downstream reporting needs.

Working knowledge of Oracle Database / Oracle Cloud or ODS-style data environments is strongly desirable. Experience with Talend or comparable ETL tooling is desirable.

Experience with scheduling / orchestration tools such as Chronicle or comparable schedulers is desirable.

Experience with PostgreSQL as a data source or operational database is desirable.

Databricks awareness or experience is desirable, especially in relation to global data platform integration.

Google Looker / Looker Studio awareness is desirable. Working knowledge of ITIL incident, problem, change and release practices for production data services.

Strong understanding of data quality, metadata, lineage, documentation and downstream data impact. Strong awareness of data privacy, access control, audit logging and data classification. Ability to work with external suppliers managing part of the data platform landscape.

Ability to work independently in an international remote / hybrid GBS model.

English C1 required.

Education Requirements

• At minimum a demonstrable bachelor-level working and thinking ability in IT, data engineering, computer science, information management or equivalent experience.

• Strong working knowledge of SQL, Azure SQL / SQL Server and data warehouse concepts required.

• Strong working knowledge of Azure Data Factory required.

• Working knowledge of Azure DevOps for backlog, repositories, deployment tracking and controlled release management required.

• Working knowledge of ETL/ELT concepts, staged data pipelines and data

quality monitoring required.

• ITIL Foundation or demonstrable ITIL working experience required.

• Oracle Cloud / Oracle Database knowledge strongly desirable due to the current ODS landscape. • Talend ETL experience desirable.

• Power BI data consumption knowledge desirable; Google Looker / Looker Studio awareness is a plus.

• PostgreSQL knowledge desirable.

• Databricks awareness or experience desirable.

• Microsoft Azure Data Engineer, Azure Database, Azure Fundamentals, Power BI, Databricks, Oracle or data warehouse certifications are desirable

Minimum Skills to Hire

• Strong Azure SQL / SQL Server experience.

• Strong SQL / T-SQL skills.

• Strong Azure Data Factory experience.

• Experience with staged data warehouse architecture.

• Experience with dimensional modelling, fact/dimension design and data marts.

• Understanding of ETL/ELT pipeline design, monitoring, dependencies and recovery.

• Understanding of Power BI data consumption, refresh dependencies and semantic model needs.

• Azure DevOps working knowledge.

• Working knowledge of ITIL incident, problem and change management.

• Strong data quality, lineage and documentation discipline.

• Awareness of data privacy, access control and audit logging.

• English C1.

• Ability to work independently in an international remote / hybrid environment.