Data Engineer

Role: Data Engineer
Duration: CTH

Key experience we're looking for:

Strong Data Engineering background
Hands-on experience with Circana (IRI) and/or IQVIA data
Consumer Products and Retail analytics experience
Marketing data and analytics
Revenue Growth Management (RGM) experience preferred
Experience building and supporting complex data pipelines and integrations
Ability to work closely with business stakeholders, analysts, architects, and data science teams
Experience with modern cloud data platforms

Mandatory
6-15 years of experience in Data Engineering.
Hands-on experience with Circana (IRI) and/or IQVIA data.
Strong experience in Consumer Products (CPG) and Retail Analytics.
Experience supporting Marketing Analytics initiatives.
Proven expertise in designing and supporting complex data pipelines and integrations.
Experience working with modern cloud-based data platforms.
Strong SQL and data modeling skills.
Experience working with large-scale structured and unstructured datasets.
Ability to collaborate effectively with business stakeholders, analysts, architects, and data science teams.

Preferred
Experience in Revenue Growth Management (RGM) programs and analytics.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Exposure to data warehousing, data lakes, and modern analytics ecosystems.
Understanding of data governance and master data management principles.
Experience in Agile delivery environments.

Key Competencies
Data Engineering & Integration
Consumer Products & Retail Domain Knowledge
Circana (IRI) / IQVIA Data Expertise
Marketing Analytics
Revenue Growth Management (RGM)
Stakeholder Management
Analytical Problem Solving
Cloud Data Platforms
Data Quality & Governance
Communication & Collaboration

Must-Have Skills
1. Microsoft Fabric Expertise
This is non-negotiable.
OneLake
Lakehouse
Data Warehouse
Fabric Data Factory
Fabric Pipelines
Spark Notebooks
Direct Lake
Power BI Semantic Models
Fabric Administration
CI/CD in Fabric

2. PySpark & Delta Lake
This is the backbone of large-scale retail harmonization.
PySpark
Delta Tables
Delta Lake
Data transformations at scale
Incremental processing
Merge/upsert patterns
Data quality frameworks

3. Retail / CPG Data Expertise
Honestly, this may be more important than Fabric experience - Look for someone who has worked with:
Circana (IRI)
IQVIA
Nielsen / NIQ
Retail Link
Club/Drug/Mass channels
POS data
Syndicated market data
Household panel data
RGM data

4. Data Harmonization & MDM
This is the skill that separates a good engineer from one who can solve your problem.
They should understand:
Product hierarchies
Identity resolution
SKU matching
Golden records
Attribute precedence
Data stewardship processes
MDM concepts
Ontology design
Examples:
Cross-country SKU harmonization
GTIN/EAN matching
Fuzzy matching
Product normalization
Attribute survivorship rules

5. Data Modeling
The engineer should be strong in:
Star schemas
Snowflake schemas
Kimball methodology
Semantic models
Fact/dimension design
Slowly changing dimensions
Hierarchies

6. SQL (Advanced)
Not just SQL.
They should be able to:
Tune large queries
Build analytical models
Create reusable views
Implement data quality checks
Optimize Fabric warehouses

7. Azure & DevOps
For production-grade delivery:
Azure Storage
DevOps
Git
CI/CD
Infrastructure-as-Code
Bicep or Terraform
Highly Desirable Skills
Revenue Growth Management (RGM)
Someone who understands:
Elasticity
Mix
Pricing
Promo effectiveness
Trade Spend
TPM
VF (Visualfabriq)
Marketing Data
Experience with:
Digital analytics
CRM
Salesforce
Retail Media
Customer Data Platforms
Attribution
Audience segmentation