Principal Data Engineer

• 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

• 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)

Experis® is a global leader in technology services and part of the ManpowerGroup® (NYSE: MAN) family of brands. We connect skilled technology professionals with meaningful opportunities where they can grow their careers, expand their expertise and make a real impact. Guided by the belief that it takes Human Ingenuity to unlock the true potential of technology, Experis helps people gain experience with leading organizations, build sought-after skills and take the next step toward a brighter future. Backed by ManpowerGroup’s 75+ years of workforce expertise and global reach across more than 70 countries and territories, Experis brings the scale, insight and opportunity to help people and businesses thrive. ManpowerGroup is consistently recognized for its commitment to inclusion, ethics and responsible business, including being named one of the World’s Most Ethical Companies. Learn more at Experis.com.