job summary
The Data Engineer - Finance Systems & Analytics plays a critical strategic role bridging the gap between Finance and Information Technology. In this position, you will transform complex business data requirements into high-performing, scalable technical solutions.
You will lead the prototyping, validation, and optimization of finance data workflows across the Bronze, Silver, and Gold layers within Microsoft Fabric and Power BI environments. This role is essential for driving data accuracy, enhancing reporting efficiency, and scaling analytical models supporting core Finance operations and reporting workflows.
location: Telecommute
job type: Contract
salary: $90 - 95 per hour
work hours: 8am to 5pm
education: Bachelors
responsibilities
Partner closely with Finance leadership and business stakeholders to translate complex requirements into scalable data models and analytical transformations.
Prototype, build, and test ETL/ELT pipelines prior to IT deployment, ensuring strict alignment with Finance's analytical and operational requirements.
Design, optimize, and document robust finance data models within Microsoft Fabric and Power BI to support enterprise reporting.
Direct data validation and continuous refinement for critical financial workflows and operational reporting models.
Collaborate with core IT engineering teams to transition validated models into production-grade ETL pipelines.
Proactively audit, identify, and resolve inconsistencies related to data quality, lineage, and transformation logic.
qualifications
Required Skills & Experience
Expert-level proficiency in SQL, Python, and Power BI.
Hands-on experience developing within Microsoft Fabric or equivalent enterprise data engineering platforms.
Proven expertise in ETL/ELT pipeline development, dimensional data modeling, and rigorous data validation methodologies.
Demonstrated ability to bridge technical and non-technical stakeholders across Finance and IT units.
Exceptional analytical, problem-solving, and troubleshooting skills with high attention to detail.
Preferred Qualifications
Prior experience in data engineering, data science, or BI within a Finance, Fintech, or corporate analytics department.
In-depth understanding of Data Lakehouse architectures and multi-layer data pipelines (Bronze/Silver/Gold paradigms).
Practical background in financial reporting structures, AR workflows, and performance metric analyses.
skills
scalable data models
Data Lakehouse
ELT pipelines
pipeline development
data pipelines
data validation
dimensional data
ETL
ETL pipelines
data engineering
Microsoft Fabric
Power BI
Python
proficiency in SQL
IT engineering
analytical
attention to detail
leadership
problem-solving
troubleshooting skills
Proactively
financial reporting
audit
core Finance
prototyping
Prototype
data models
data accuracy
data quality
data science
Fintech
Invoice
finance
analytical models
modeling
performance metric
workflows