Allianz Life Insurance

Data Engineer Principal_3003

Allianz Life Insurance • $110K — $150K *
Minneapolis, MN 55407In-Person
Information Technology
5 - 7 years of experience
Today
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Job Overview by Ladders

Qualifications

  • 7+ years of hands-on experience in Data Engineering, focusing on complex enterprise data platforms
  • Proven track record in migration or re-engineering work rather than starting from scratch
  • Deep understanding of data governance, including audit trails and reconciliation
  • Strong analytical skills for identifying implementation flaws and making re-engineering decisions
  • Excellent communication skills for collaborating with diverse stakeholders

Responsibilities

  • Contribute to the re-engineering of the enterprise data platform across all layers
  • Reverse-engineer and validate existing data pipeline logic and models
  • Identify and remediate technical debt and defects within the platform
  • Ensure the integrity of data processing patterns during implementation
  • Support the transition to a modern data architecture while preserving existing workflows
  • Execute the migration of workloads to Databricks while maintaining governance standards
  • Mentor junior engineers and assist in the decommissioning of legacy components

Benefits

  • Hybrid work model promoting balance between in-person and remote work
  • Health insurance and 401(K) with company match
  • Company bonus scheme and paid vacation days
  • Tuition reimbursement and paid parental leave
  • Employee shares program and discounts
  • Access to career development and digital learning programs for lifelong learning
Full Job Description
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, 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
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  • 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 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 versioning
  • Strong analytical mindset: ability to read existing implementations, identify intent versus defect, and make sound re-engineering decisions
  • Primarily hands-on, while comfortable contributing to technical design discussions
  • 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)


What we offer
  • We offer a hybrid work model which recognizes the value of striking a balance between in-person collaboration and remote working
  • We believe in rewarding performance and our compensation and benefits package includes health insurance, company bonus scheme, 401(K) with company match, company paid holidays, paid time off, paid volunteer days, tuition reimbursement, paid parental leave, an employee shares program and employee discounts From career development and digital learning programs to international career mobility, we offer lifelong learning for our employees worldwide and an environment where innovation, delivery and empowerment are fostered.
  • The annualized base pay range for this role is $110,000 - 150,000. The annual base salary range represents a nationwide market range. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role, and the skills, education, training, credentials and experience of the candidate. The base pay is just one component of the ATA total compensation package. As part of our comprehensive compensation and highly rated benefits programs, ATA also offers eligibility for an incentive-based annual bonus.


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About Allianz Life Insurance

Allianz Life Insurance Company of North America is a leading provider of retirement solutions, including fixed and variable annuities and life insurance for individuals. The company is part of Allianz SE, a global financial services company that is headquartered in Munich, Germany. Allianz Life Insurance Company of North America was founded in 1896 and has been providing financial protection and retirement solutions to Americans for over 125 years. The company has a strong commitment to corporate responsibility and sustainability, and has been recognized for its efforts in these areas. Allianz Life Insurance Company of North America is a subsidiary of Allianz Life Insurance Company of New York, which is a wholly owned subsidiary of Allianz SE.
Learn more about Allianz Life Insurance
Size
142,460 employees
Industry
Finance & Insurance
Founded
1890
NASDAQ
AZSEY
* Ladders Estimates

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