Senior Data Engineer

Allianz Services; Maharashtra; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Allianz Services is seeking a Senior Data Engineer / Platform Re-Engineering Lead to drive migration from Azure Synapse to Databricks. You will audit and reverse‑engineer an enterprise data platform, define migration strategy, and ensure governance remains intact.

The role requires hands‑on expertise in Azure Synapse, Databricks, Delta Lake, and data governance, with strong Python, SQL, and Spark skills. You will lead migration phases, validate outputs, and collaborate with governance and

Qualifications

• 7+ years of experience in Data Engineering with migrations or re-engineering.
• Proven track record auditing and owning complex enterprise data platforms.
• Deep knowledge of data governance patterns and SCD versioning.
• Ability to read existing implementations and distinguish intent from defects.
• Comfortable with hands-on engineering and technical architecture.

Responsibilities

• Lead the assessment and re-engineering of an enterprise data platform across ingestion to consumption.
• Reverse-engineer, document, and validate pipeline logic, data models, and governance.
• Identify gaps and technical debt; remediate sub-optimal implementations.
• Ensure correctness of CDC, SCD, deduplication, and business reconciliation.
• Design target architectures aligned to lakehouse principles with parity during transitions.
• Manage migration strategies and parallel-run validation before cutover.
• Re-implement pipelines on target platforms with audit and reconciliation.
• Collaborate with governance and architecture stakeholders on rules and quality.
• Provide technical leadership across workstreams and decommission legacy components.

Skills

Azure Synapse Analytics

Azure Databricks

PySpark

SQL

Python

Spark

Data Modeling

Data Governance

CI/CD

Docker

Tools

Delta Lake

Unity Catalog

Power BI

Terraform

ARM

GitHub Actions

Job description

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 Responsibilities

• Lead 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
• Design and implement target‑state architectures aligned to modern lakehouse principles, ensuring feature parity and business logic fidelity during transitions
• Manage platform evolution initiatives, including parallel‑run phases where multiple implementations operate simultaneously, validating output consistency before cutover
• Define and execute migration strategies for 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
• Provide technical leadership across re‑engineering and migration workstreams, contributing to 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, PySpark, SQL‑driven generation from mapping documents)

Databricks & Lakehouse

• Hands‑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 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

DevOps & Automation

• CI/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 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 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 & Profile

• 7+ years of experience in Data Engineering, with significant platform migration or re‑engineering experience
• Proven track record auditing and taking ownership of existing, complex enterprise data platforms — not just building from scratch
• Deep knowledge of enterprise data governance patterns: audit trails, reconciliation, data quality controls, SCD versioning
• Strong analytical mindset: ability to read existing implementations, identify intent versus defect, and make sound re‑engineering decisions
• Comfortable operating across both hands‑on engineering and technical architecture
• Strong communication skills — able to engage business, governance, and engineering stakeholders with clarity
• Experience working in regulated or enterprise‑scale environments (financial services a plus)