Job Description – Senior Data Engineer / Platform Re-Engineering Lead (Azure Synapse & Databricks Migration)Role Overview We are looking for a highly experienced Senior Data Engineer to lead the re-engineering of an existing enterprise data platform built on Azure Synapse Analytics. The role requires deep technical seniority to audit, understand, and validate a complex end-to-end data architecture spanning source ingestion through to consumption — and to drive a future migration of validated workloads to Databricks. This is not a greenfield role: it demands the ability to reverse-engineer existing implementations, assess their correctness, and own the technical migration strategy.Key ResponsibilitiesLead the technical assessment and re-engineering of an existing enterprise data platform, spanning all layers from source ingestion through to data consumptionReverse-engineer, document, and validate existing pipeline logic, data models, transformation frameworks, and data governance controlsIdentify gaps, defects, and technical debt across the platform and remediate where implementations are incorrect or sub-optimalEnsure correctness of data processing patterns including change data capture, slowly changing dimensions, deduplication, and business reconciliationDesign and implement target-state architectures aligned to modern lakehouse principles, ensuring feature parity and business logic fidelity during transitionsManage platform evolution initiatives, including parallel-run phases where multiple implementations operate simultaneously, validating output consistency before cutoverDefine and execute migration strategies for existing workloads to up-to-date data platforms, preserving existing governance and control framework semanticsRe-implement ingestion, transformation, and orchestration pipelines on target platforms, maintaining audit, quality, and reconciliation standardsCollaborate with business, data governance,



and architecture stakeholders to validate embedded business rules and data quality requirementsProvide technical leadership across re-engineering and migration workstreams, contributing to decommission planning for legacy componentsCore Technical SkillsAzure 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 integrationAzure Analysis Services and Power BI consumption layer patternsDeep understanding of medallion architecture: Raw / Harmonized / Conformed / Consumption layersStrong knowledge of SCD Type 0/1/2, CDC patterns, soft/hard delete, and retroactive change processingExperience with Synapse SQL Pool — stored procedures, control tables, and data quality validation patternsExperience with audit, balance, and control frameworks — parameterized, modular pipeline governance at enterprise scaleFamiliarity with config-driven and automation-first pipeline patterns (YAML, PySpark, SQL-driven generation from mapping documents)Databricks & LakehouseHands-on experience with Azure Databricks (Delta Live Tables, Unity Catalog preferred)Strong Apache Spark skills (PySpark / Spark SQL)Experience migrating workloads from legacy data warehouse or Synapse environments to a Databricks LakehouseAbility 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 enforcementData Engineering & DevelopmentStrong Python and SQL programming skillsExperience with ETL/ELT at scale: denormalization, surrogate keys, directory tables, curated data modelsExperience integrating complex data sources: Oracle DB, SQL Server, Azure SQL DB, file systems, Salesforce, APIsStrong data modelling skills: relational, dimensional, and lakehouse-orientedDevOps & AutomationCI/CD pipelines for data engineering (Azure DevOps / GitHub Actions)Infrastructure as Code (Terraform or ARM)Containerization (Docker)Experience with automated testing frameworks for data pipelines (unit testing, reconciliation-based validation)Nice to HaveExperience with Unity Catalog for data governance and lineageFamiliarity with Azure Purview for data cataloguing and governanceExposure to real-time and streaming pipelines (Event Hub / Kafka / Kinesis)Experience with GenAI 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 & Profile7+ years of experience in Data Engineering, with significant platform migration or re-engineering experienceProven track record auditing and taking ownership of existing, complex enterprise data platforms — not just building from scratchDeep knowledge of enterprise data governance patterns: audit trails, reconciliation, data quality controls, SCD versioningStrong analytical mindset: ability to read existing implementations, identify intent versus defect, and make sound re-engineering decisionsComfortable operating across both hands-on engineering and technical architectureStrong communication skills — able to engage business, governance, and engineering stakeholders with clarityExperience working in regulated or enterprise-scale environments (financial services a plus)

📌 Senior Data Engineer (Maharashtra)
🏢 Allianz Services
📍 Maharashtra