Amazon Redshift Experts in Frankfurt

• Led design, development, and deployment of data solutions supporting a major pharma acquisition for Takeda Pharmaceutical Company, delivering transparency reporting systems across Azure,Databricks (Python and Shell Scripting) platforms.
• Owned,Designed and developed scalable ELT pipelines to process Customer and Product data using Azure, complex SQL, Databricks, and shell scripting, enabling efficient data integration and processing across multiple sources including job orchestration and workflow automation.
• Implemented performance optimization techniques (query tuning, parallelism, workload optimization), improving system efficiency and processing time.
• Applied strong analytical and problem-solving skills to assess technical solutions and support business requirements for compliance and transparency reporting.
• Designed scalable data foundations suitable for downstream analytics and AI workloads.
• Led data quality initiatives by assessing multiple source data, defining quality metrics, and establishing processes for monitoring and continuous improvement.

• Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
• Independently designing analytics solutions with Python, SQL, etc.
• Designing and implementing ETLs and data pipelines
• Creating and maintaining APIs
• Independently applying CI/CD, testing, and version control
• Data modeling
• Model development and optimization
• Anomaly detection with AI
• Predictive analytics

Used technologies:

• Snowflake
• Fabric
• Azure Synapse Analytics
• Azure DataFactory
• Azure Data Lake
• Azure DevOps
• Databricks
• Spark
• CI/CD
• SQL Database
• Python
• Power Platform

• Analysis of web and campaign performance to derive actionable insights for marketing and growth optimization
• Implementation and maintenance of tag management solutions to ensure reliable and consistent data collection
• Continuous development and optimization of reporting structures with a focus on scalability and data quality
• Conducting regular deep-dive analyses and leading monthly stakeholder sessions to present results and align on optimization measures
• Design and operation of end-to-end data flows from data collection to visualization
• Tools: GA4, Google Tag Manager, Looker Studio, Airbyte, BigQuery

• Development of a central billing app for cloud costs at DB
• AWS
• Python
• AWS CDK
• RDS
• Spark (PySpark)
• Glue
• Lambda
• CI/CD (GitLab)
• React/Typescript
• data optimization
• Scrum

• Global marketing analytics for Hitachi Energy as part of a global data modernization initiative aiming to enhance data retention, historical data availability and provide Eloqua's 2-year retention for remote interaction reporting and analytics.

• Analyzed Eloqua's default retention policy and identified risk of data loss for records older than two years.

• Designed and implemented historical data preservation strategy by creating transformed tables in the target data platform to archive older data while ensuring data quality dashboards.

• Collaborated with the Power BI team to re-point dashboards from raw Eloqua imports to the newly created archival layer.

• Leveraged Jira to track and manage data engineering tasks, bugs, and feature requests across Agile sprints; coordinated backlog prioritization and task assignment to align data pipeline development with business needs.

• Power BI dashboard optimization:

• Worked closely with business stakeholders to assess and understand reporting needs for reverse customer data.

• Designed and implemented incremental refresh in Power BI to ensure daily updates without full data reloads.

• Collaborated with Azure data engineers to optimize data processing and publication pipelines.

• Stakeholder communication & data modeling:

• Acted as liaison between Group Data Office and Technology Office to align data modelling standards.

• Gathered requirements from data engineering team and participated in weekly status meetings to provide implementation updates and resolve blockers across teams in Germany, Poland, and India.

• Documentation & quality assurance:

• Prepared end-to-end technical design documentation, data flow diagrams, and Power BI audit guides for future reference.

• Participated in UAT sessions with business users to validate data outputs and report accuracy.

• Evaluation of potential providers (Snowflake vs Databricks) and design of the analytics data platform using Databricks
• Data storage and ingestion layer with Amazon S3
• Creation of ETL processes and data transformations with AWS Glue and Databricks Notebooks
• Orchestration with AWS Glue Workflow, Databricks Workflow and Databricks DLT
• Processing of unstructured data, including text, images and video
• Databricks workspace setup and administration
• Implementation of a medallion architecture to ensure data quality
• Evaluation of potential BI tools (Power BI, AWS Quicksight, Tableau)
• Establishment of MLOps using MLFlow
• Introduction of data governance and data lineage using Unity Catalog