Data engineer principal_3003

Data Engineer Principal_3003

Salary:
€110,000
- 150,000 yearly Azienda: Allianz Tipo Lavoro: Full Time Italy Descrizione Lavoro
- Data Engineer Principal_3003 We are looking for an experienced Principal Data Engineer to work hands-on on the re-engineering of an existing enterprise data platform built on Azure Synapse Analytics. The role requires strong technical depth to audit, understand, and validate a complex end-to-end data architecture spanning source ingestion through to consumption — and to help deliver the migration of validated workloads to Databricks. This is hands on role and demands the ability to reverse-engineer existing implementations, assess their correctness, and build to an agreed migration strategy.

Key Responsibilities

Contribute to the technical assessment and re-engineering of an existing enterprise data platform, spanning all layers from source ingestion through to data consumption Reverse-engineer, document, and validate existing pipeline logic, data models, transformation frameworks, and data governance controls Identify gaps, defects, and technical debt across the platform and remediate where implementations are incorrect or sub-optimal Ensure correctness of data processing patterns including change data capture, slowly changing dimensions, deduplication, and business reconciliation Implement target-state designs aligned to modern lakehouse principles, ensuring feature parity and business logic fidelity during transitions Support platform evolution initiatives, including parallel-run phases where multiple implementations operate simultaneously, validating output consistency before cutover Execute migration of existing workloads to modern data platforms, preserving existing governance and control framework semantics Re-implement ingestion, transformation, and orchestration pipelines on target platforms, maintaining audit, quality, and reconciliation standards Collaborate with business, data governance, and architecture stakeholders to validate embedded business rules and data quality requirements Mentor less experienced engineers, review code and designs, and support decommission planning for legacy components Core Technical Skills Azure Synapse & Data Platform Mandatory hands-on expertise with: Azure Synapse Analytics (Pipelines, Spark Pool, Dedicated SQL Pool) Azure Data Lake Storage Gen2 (ADLS Gen2) Delta Lake on Azure (Synapse Lakehouse patterns) Oracle Golden Gate Replication for real-time source integration Azure Analysis Services and Power BI consumption layer patterns Deep understanding of medallion architecture: Raw / Harmonized / Conformed / Consumption layers Strong knowledge of SCD Type 0/1/2, CDC patterns, soft/hard delete, and retroactive change processing Experience with Synapse SQL Pool — stored procedures, control tables, and data quality validation patterns Experience with audit, balance, and control frameworks — parameterized, modular pipeline governance at enterprise scale Familiarity with config-driven and automation-first pipeline patterns (YAML, Py Spark, SQL-driven generation from mapping documents) Databricks & Lakehouse Hands-on experience with Azure Databricks (Delta Live Tables, Unity Catalog preferred) Strong Apache Spark skills (Py Spark / Spark SQL) Experience migrating workloads from legacy data warehouse or Synapse environments to a Databricks Lakehouse Ability to re-implement governance and control frameworks natively in Databricks (audit logging, reconciliation, DQ checks) Experience with Delta Lake features: MERGE, CDC, time travel, schema enforcement Data Engineering & Development Strong Python and SQL programming skills Experience with ETL/ELT at scale: denormalization, surrogate keys, directory tables, curated data models Experience integrating complex data sources: Oracle DB, SQL Server, Azure SQL DB, file systems, Salesforce, APIs Strong data modelling

skills:
relational, dimensional, and lakehouse-oriented Dev Ops & Automation CI/CD pipelines for data engineering (Azure Dev Ops / Git Hub Actions) Infrastructure as Code (Terraform or ARM) Containerization (Docker) Experience with automated testing frameworks for data pipelines (unit testing, reconciliation-based validation) Nice to Have Experience with Unity Catalog for data governance and lineage Familiarity with Azure Purview for data cataloguing and governance Exposure to real-time and streaming pipelines (Event Hub / Kafka / Kinesis) Experience with Gen AI or ML platform integration (MLOps, feature engineering pipelines) Familiarity with monitoring and observability tools (e.g., Dynatrace) Exposure to BI tools (Power BI, Tableau) Experience & Profile 7+ years of hands-on experience in Data Engineering, including platform migration or re-engineering work Proven track record working on existing, complex enterprise data platforms — not just building from scratch Deep knowledge of enterprise data governance patterns: audit trails, reconciliation, data quality controls, SCD ve