Senior Data Architect

Your career at Deutsche Börse Group\n\nYour area of work:

As a Data Architect at Clearstream Securities Services, you will play a key role in driving our digital transformation by shaping the data architecture that underpins strategic initiatives such as Data & AI, the D7 digital securities platform, the modernization of our Digital CSD, and our cloud adoption journey. Your work will ensure that Clearstream's data ecosystem is secure, scalable, and future-ready, enabling advanced analytics, regulatory compliance, and innovative services for global financial markets. By defining enterprise standards and collaborating across domains, you will help position Clearstream as a leader in digital finance and data excellence. Your responsibilities:

• Define the target-state cloud data architecture (Lakehouse) across BI, analytics, and ML-including reference architectures, patterns, and guardrails.
• Lead domain-driven and Data Mesh architecture: curate federated data product standards, contracts, and interoperability guidelines.
• Establish architecture decision records (ADRs) and technology roadmaps (near/mid/long term), aligning with enterprise data strategy and regulatory requirements.
• Act as a trusted advisor to data scientists, analysts, and business product teams; unlock self-service patterns and reuse.
• Lead onboarding, training, and enablement programs to scale platform and catalog adoption; raise data literacy across domains.
• Mentor engineers and stewards; foster a culture of automation, reliability, and measurable outcomes.
• Guide the design and configuration of Atlan workspaces, clusters, compute policies, and libraries; ensure high availability, scalability, and cost efficiency.
• Own enterprise metadata strategy-ingestion, enrichment, and stewardship-leveraging Atlan (or equivalent) for catalog, lineage, glossary, and data contracts.
• Define data quality frameworks (profiling, rules, SLAs, monitoring) using solutions like Soda/Great Expectations; embed DQ controls into pipelines.
• Establish critical data element (CDE) standards, certification workflows, and KPIs; publish management reporting on catalog adoption and DQ outcomes.

Your profile:

• 8-12+ years in data architecture/engineering within cloud big data platforms (GCP preferred), including enterprise and regulated environments.
• Proven leadership in Databricks/Spark, Lakehouse architectures, and operationalizing Unity Catalog at scale.
• Hands-on track record with metadata/catalog platforms (Atlan/Collibra) and data quality frameworks (Soda/Great Expectations), including enterprise rollout and adoption.
• Demonstrated success aligning IT, business, compliance, and security to implement governed, high-quality data solutions.
• Deep understanding of data modeling, data contracts, DQ SLAs, lineage, privacy/security, and observability (e.g., Azure Monitor/Datadog).
• Working knowledge of ML lifecycle (MLflow, feature stores) and semantic/metrics layers for analytics products.
• Executive-level communication, stakeholder engagement, and influence; comfortable with board-level reporting and audit dialogue.
• Strong facilitator and coach; able to drive consensus across diverse technical and business teams.
• Fluent in English; additional language skills are a plus.
• Certifications (preferred)
• Azure Data Engineer/Architect; Databricks Data Engineer Professional/Architect; TOGAF; CDMP (DAMA); Security/Compliance certifications are advantageous.

• 8-12+ years in data architecture/engineering within cloud big data platforms (GCP preferred), including enterprise and regulated environments.
• Proven leadership in Databricks/Spark, Lakehouse architectures, and operationalizing Unity Catalog at scale.
• Hands-on track record with metadata/catalog platforms (Atlan/Collibra) and data quality frameworks (Soda/Great Expectations), including enterprise rollout and adoption.
• Demonstrated success aligning IT, business, compliance, and security to implement governed, high-quality data solutions.
• Deep understanding of data modeling, data contracts, DQ SLAs, lineage, privacy/security, and observability (e.g., Azure Monitor/Datadog).
• Working knowledge of ML lifecycle (MLflow, feature stores) and semantic/metrics layers for analytics products.
• Executive-level communication, stakeholder engagement, and influence; comfortable with board-level reporting and audit dialogue.
• Strong facilitator and coach; able to drive consensus across diverse technical and business teams.
• Fluent in English; additional language skills are a plus.
• Certifications (preferred)
• Azure Data Engineer/Architect; Databricks Data Engineer Professional/Architect; TOGAF; CDMP (DAMA); Security/Compliance certifications are advantageous.