Senior Data Engineer

We are looking for a Senior Data Engineer to support a data platform initiative, with a strong focus on data discovery, profiling, validation, business reporting alignment, business analysis, and downstream data engineering enablement in a business environment related to pharmaceutical distribution, medication sales, inventory control, and regulated product data.

This senior role requires someone who can bridge business stakeholders and technical teams, clarify reporting and data requirements, validate data quality, document data assets, and contribute to the design of scalable ETL/ELT pipelines and data warehouse structures that support trusted BI and decision-making.

1. Data Discovery, Profiling & Business Understanding

• Explore and analyze data from key systems (Magento, PK Software, Microsoft Business Central) and build a clear understanding of the data landscape
• Profile datasets and tables by reviewing row counts, null rates, value distributions, key relationships, and data anomalies
• Identify data sources, data owners, refresh frequency, business context, entities, relationships, and reporting gaps
• Work with stakeholders to understand business processes, reporting pain points, and decision-making needs
• Understand domain-specific data flows related to pharmaceutical sales, product master data, batch and expiry tracking, inventory movement, and compliance-sensitive reporting

2. Data Catalog, Metadata & Business Definition

• Build and maintain structured documentation for data assets, including tables, fields, data types, relationships, and lineage where applicable
• Document field meanings, business definitions, and glossary terms in language accessible to both technical and non-technical audiences
• Keep metadata and business documentation up to date as the data model and reporting needs evolve
• Gather, analyze, and document business requirements, data requirements, and reporting expectations

3. Data Quality & Validation

• Define and execute data validation and reconciliation rules
• Identify inconsistencies, duplicates, missing data
• Ensure alignment with trusted data sources

4. Data Integration, ETL / ELT & DWH Support

• Validate data mapping, transformation logic, and target-model alignment for ETL / ELT pipelines
• Support the design, testing, and validation of reusable pipelines, incremental loads, and reliable data integration patterns
• Support creation of reusable reporting datasets and Data Warehouse (DWH) -ready structures that enable scalable BI and analytics

5. Reporting, KPI Validation & BI Layer Support

• Validate PowerBI dashboards and reports
• Ensure KPI accuracy (revenue, margin, inventory, etc.)
• Support UAT and business validation sessions
• Support semantic layer, dataset, and reporting-model readiness in collaboration with BI and business stakeholders
• Lead requirement clarification sessions, support gap analysis, and translate business needs into actionable data/reporting specifications

6. Data Governance Support

• Support definition of data ownership and usage rules
• Contribute to improving data transparency and governance