HeadSpin
Website:
headspin.io
Job details:
Data Engineer
INDIVIDUAL CONTRIBUTOR ROLE
The Data Engineer operates within the framework established by the Lead — designing, building, and maintaining robust data pipelines and transformation logic that power analytics, compliance, and operational reporting across the Mortgage Cadence Platform. The role is execution-focused with increasing ownership of end-to-end data workflows as familiarity with the platform grows. Strong SQL, ETL, and data quality skills are required; the ability to build reports and leverage semantic models is secondary to data engineering excellence.
CORE RESPONSIBILITIES
DATA PIPELINE DEVELOPMENT
- Design and build extraction, transformation, and loading (ETL) pipelines using Microsoft Fabric (Dataflow Gen2, Notebooks, or equivalent tools)
- Write optimized SQL queries and transformations for data ingestion from designated source systems
- Apply data quality rules and validation logic at each pipeline stage
- Implement incremental loads and manage refresh schedules for performance
- Escalate to Lead for architectural decisions or complex transformation patterns
DATA QUALITY & VALIDATION
- Define and implement data quality checks at ingestion, transformation, and output stages
- Perform ongoing data validation to ensure pipeline outputs align with business logic and source system expectations
- Identify, document, and escalate data quality issues with root cause analysis
- Maintain data quality dashboards and SLA monitoring
- Support UAT for new data sources or transformation logic
TRANSFORMATION & MODELING
- Build and maintain data transformations using Power Query, SQL, or Python as appropriate
- Develop dimensional models and define aggregation logic aligned with analytics requirements
- Optimize data structures for performance and maintainability
- Document transformation logic, lineage, and assumptions per team standards
- Collaborate with Lead to define semantic models and calculated metrics
OPERATIONAL SUPPORT
- Troubleshoot pipeline failures and performance issues; coordinate resolution with IT/Engineering
- Respond to data discrepancy reports from business users and analysts
- Maintain documentation of data sources, data dictionaries, and transformation specifications
- Support capacity planning and optimization of Fabric environments and pipelines
REQUIRED SKILLS
Technical
- Advanced SQL — query optimization, window functions, performance tuning, debugging complex transformations
- Proficient with Microsoft Fabric — (Dataflow Gen2, Notebooks, Lakehouse) OR equivalent ETL tools (Python, dbt, Talend, Informatica)
- Strong understanding of relational database design and dimensional modeling
- Power Query / M — complex data shaping, merging, error handling, and transformation logic
- Python or similar scripting language — data manipulation, pipeline automation
- Git/version control basics — able to collaborate on code and track changes
- Data quality and testing frameworks — unit tests, assertions, validation rules
Functional
- Ability to interpret business requirements and design efficient data solutions
- Data governance mindset — understands data lineage, documentation, and quality standards
- Proactive about identifying edge cases and potential data issues
- Mortgage/lending domain familiarity preferred; willingness to learn domain required
- Works effectively within defined standards and escalates architectural questions to Lead
- Able to balance speed with quality; advocates for technical excellence
COMMUNICATION REQUIREMENTS BY STAKEHOLDER
Analytics / BI Team
Data pipeline requirements, data quality issues, model design collaboration
- Translate analytical requirements into robust data solutions
- Communicate data lineage and transformation logic clearly
- Document assumptions and limitations of data sources and transforms
- Set realistic timelines for new pipelines or data source onboarding
Data Lead
Daily collaboration, code/design review, escalation of technical blockers
- Provide detailed status updates on assigned pipelines; flag performance or quality concerns early
- Document design decisions and trade-offs for Lead review — escalate architecture questions rather than assume
- Demonstrate commitment to code quality and maintainability; accept technical feedback constructively
IT / Engineering
Data access provisioning, source system clarifications, infrastructure support
- Communicate data requirements precisely — schema details, volume expectations, refresh frequency
- Escalate data access or infrastructure needs through Lead; provide business context
- Provide detailed defect reports with query examples and expected vs. actual results
Business / Operations
Data quality escalations, new data source requests
- Explain data quality issues and timelines in business terms; avoid over-technical language
- Ask clarifying questions about data requirements and business logic expectations
- Set expectations transparently; communicate delays or blockers early through Lead
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