We are looking for a seasoned Data Architect to own the data architecture and product delivery strategy for our retail and downstream business. Operating at the intersection of local business insight and global IT strategy, you will design scalable, well-governed data platforms that translate commercial needs into robust, business-ready data products.
You will work closely with both business and IT stakeholders bridging local operational context with the global data landscape and remain accountable for the architectural quality, reliability, and business relevance of every data product you deliver.
Must-have: Databricks (Lakehouse, Unity Catalog, Delta Live Tables), AWS (S3, Glue, Redshift, Lake Formation), Azure (ADLS, Synapse, ADF, Purview), Apache Airflow, Apache Kafka, dbt, SQL and Python.
Nice to have: Snowflake and other modern data platform and orchestration tooling
- Design and own end-to-end data architecture for retail and downstream data products from ingestion to consumption layer.
- Define data modeling standards, platform patterns, and architectural principles across the data ecosystem.
- Lead the design and delivery of data products, ensuring they are reusable, well-documented, and fit for business consumption.
- Translate local business requirements into architecture decisions aligned with the global IT strategy and data governance framework.
- Act as the technical bridge between business stakeholders and global IT, ensuring local context is reflected in platform design.
- Drive data quality, lineage, and cataloguing practices across the full data landscape.
- Evaluate and recommend new data technologies, architectural patterns, and tooling approaches.
- Mentor and guide data engineers and analysts, setting architectural guardrails and best practices.
- Engage with global data and IT teams to align on standards, tooling roadmap, and delivery priorities.
- 10+ years of experience in data architecture, data engineering, or data platform roles, with at least 5 years in a senior architecture capacity.
- Deep hands-on expertise with Databricks: Lakehouse design, Delta Lake, Unity Catalog, and data pipeline orchestration.
- Strong cloud platform experience across AWS and Azure storage, computing, orchestration, and governance services.
- Proven track record designing and delivering data products governed, business-ready data assets, not just pipelines.
- Solid command of data modeling techniques: dimensional modeling, Data Vault, or equivalent.
- Hands-on experience with Apache Airflow, designing, deploying, and maintaining complex workflow orchestration at scale.
- Hands-on experience with Apache Kafka event streaming architecture, topic design, producers/consumers, and real-time data pipeline patterns.
- Solid dbt experience data transformation layer design, testing frameworks, documentation practices, and integration with modern data stacks.
- Strong SQL expertise, complex query writing, performance tuning, and data modeling applied directly in analytical and operational contexts.
- Python proficiency for data management, scripting, pipeline development, data wrangling, and automation across the data stack.
- Experience working across global and local IT structures, navigating enterprise standards while addressing regional business needs.
- Strong stakeholder engagement skills, able to communicate architectural decisions clearly to both technical peers and business leaders.
- Demonstrated delivery in complex, multi-stakeholder environments with measurable, business-oriented outcomes.
- Hands-on Snowflake experience: data sharing, warehousing patterns, or hybrid Snowflake/Databricks architectures.
- Familiarity with data mesh principles and domain-oriented data product ownership models.
- Knowledge of data governance frameworks and cataloguing tools (e.g., Azure Purview, Collibra, Alation).
- Experience in retail, trading, energy, or commodity-intensive industries.
- Background in BI/analytics layer delivery (Power BI, Qlik, Tableau) and the data architecture that supports it.