Role Description As a Senior Databricks Consulting Engineer, you will serve as a trusted technical advisor and strategic partner to enterprise customers. You will guide organizations through their data and AI transformation journeys, helping them design, implement, and optimize modern data platforms built on Databricks. You will combine deep technical expertise with strong stakeholder management skills to ensure customers realize measurable value from their Databricks investments. Working alongside client teams, executives, engineers, and Ultra Tendency consultants, you will lead architecture decisions, promote best practices, and drive successful adoption of the Databricks Lakehouse Platform. This is a highly customer-facing role requiring both hands-on technical capability and executive-level communication skills. Key Responsibilities - Customer Advisory & Architecture - Act as the primary technical advisor for strategic Databricks customers. - Design and review scalable data, analytics, and AI architectures leveraging the Databricks Lakehouse Platform. - Lead architecture workshops, solution design sessions, and technical roadmap discussions. - Translate business requirements into technical solutions and implementation plans. - Advise customers on platform modernization, cloud migration, data governance, and AI initiatives. - Solution Delivery & Optimization - Support the successful implementation and adoption of Databricks solutions. - Review data engineering, analytics, machine learning, and GenAI workloads for scalability, performance, security, and cost optimization. - Identify and resolve technical blockers during customer engagements. - Drive architectural best practices for data pipelines, Lakehouse design, Unity Catalog, governance, and platform operations. - Technical Leadership - Serve as a subject matter expert on Databricks capabilities and emerging data & AI technologies. - Mentor customer teams and Ultra Tendency consultants on architecture patterns and best practices. - Deliver executive presentations, technical workshops, and enablement sessions. - Contribute to reusable assets, reference architectures, and practice development initiatives. - Business Development Support - Partner with sales and account teams during strategic customer engagements. - Support technical discovery, solution positioning, and proposal development. - Provide architecture guidance during pre-sales and proof-of-concept activities. - Identify opportunities for platform expansion and increased customer value. Qualifications - 7+ years of experience in data engineering, cloud architecture, analytics, or related fields. - 3+ years working with Databricks in enterprise environments. - Strong expertise in modern data platform architecture and cloud-native solutions. - Hands-on experience designing and implementing Lakehouse architectures. - Strong knowledge of: - Databricks Data Intelligence Platform - Apache Spark - Delta Lake - Unity Catalog - Data Governance & Security - Data Warehousing and Lakehouse Design - ETL/ELT Architectures - Data Modeling - Experience with at least one major cloud platform: - Microsoft Azure (preferred) - AWS - Google Cloud Platform - Proficiency in: - Python - SQL - Spark - Experience engaging with executive stakeholders and technical leadership teams. - Excellent communication and presentation skills in English & German. Preferred Qualifications - Databricks certifications such as: - Databricks Certified Data Engineer Professional - Databricks Certified Solutions Architect - Databricks Certified Machine Learning Professional - Experience with: - Generative AI and LLM solutions - MLflow - MLOps - Data Governance frameworks - Data Mesh architectures - Real-time and streaming data platforms - Experience in consulting or professional services environments. What Success Looks Like - Customers successfully adopt and scale Databricks solutions. - Complex technical challenges are resolved quickly and effectively. - Executive stakeholders view you as a trusted advisor. - Best practices are consistently applied across customer environments. - Customer satisfaction, platform utilization, and business outcomes improve measurably. Benefits - Work with leading enterprise customers across Europe. - Be part of a rapidly growing Data & AI consultancy. - Access to cutting-edge technologies in Data Engineering, Analytics, AI, and GenAI. - Opportunity to shape and expand Ultra Tendency's Databricks practice. - Continuous learning and certification support. - Flexible and remote-friendly working environment. - Competitive compensation package. - Collaborative, entrepreneurial, and international culture. Eligibility - Applicants must currently reside in Germany and possess the legal right to work in the country. - Unfortunately, we are unable to provide relocation assistance or visa sponsorship for this position.

Role Description Design, build, and operate the unified data platform and domain pipelines that underpin scalable, trusted, and monetisable data products. - Design and implement batch and real time ingestion pipelines from internal and external sources - Implement automated data quality checks, observability, and SLA monitoring - Support master data management, metadata, lineage, and access controls - Optimise datasets and pipelines for analytics, ML training, and API consumption - Work closely with Data Scientists and ML Engineers to support feature and model needs - Contribute to long term platform roadmap and AI readiness Qualifications - 5+ years experience in data engineering or platform roles - Strong experience with SQL, Python, Spark, and cloud platforms (AWS, Azure, or GCP) - Experience operating data platforms in production at scale - Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field - MS in Big Data, AI or relevant fields - Certifications such as PMI ACP, PMP, SAFe, CSM, or equivalent are beneficial Benefits - Flexible employment and remote work - International projects with leading global clients - International business trips - Non-corporate atmosphere - Language classes - Internal & external training - Private healthcare and insurance - Multisport card - Well-being initiatives

• Oversee and optimize the Denodo platform, defining best practices for development, governance, and performance. • Design and support new data integrations, ensuring alignment with the established architecture and standards. • Serve as the technical point of reference for the team on Denodo and BigQuery-related inquiries. • Define integration and optimization patterns across technologies such as Denodo, Snowflake, BigQuery, DBT, and data orchestration tools.

• Own and evolve the data platform - the BigQuery warehouse, dbt transformation layers, Airflow / Cloud Composer orchestration and Pub/Sub ingestion that feed every model and metric. • Build and operate the ML platform - training pipelines (Kubeflow on Vertex AI), model serving (FastAPI behind Vertex endpoints), CI/CD, containerization and typed contracts. • Take operational ownership of model-serving infrastructure so reliability isn't carried by the data scientists alone. • Harden and standardize the data models the business depends on - improving schemas, fixing data-quality issues and establishing trustworthy source-of-truth feeds. • Establish data governance and observability - bring data that lives outside the warehouse under proper governance and build operational metrics for products that don't yet have them. • Standardize how data engineering is done across product lines - patterns, tooling and pipelines other teams can adopt. • Partner across data science, backend and product on the producer to consumer contract (models produced by data science, consumed/aggregated downstream, surfaced to clients).