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Team Tagline
About the role
Helda is a Health-Tech organisation whose mission is to transform how healthcare is delivered in Nigeria, Africa and beyond by turning existing healthcare data into actionable intelligence. Through advanced analytics and AI-driven insights we aim to revolutionise healthcare by delivering better patient outcomes, slashing operational costs, and driving unprecedented efficiency across the entire organisation. Heldatech is a healthcare intelligence platform that transforms healthcare organisations data into actionable insights through AI-powered analytics. Our platform serves healthcare administrators, finance teams, and clinical leadership with real-time dashboards covering pricing intelligence, revenue performance, and patient analytics.
We are looking for a Data Engineer with backend experience to operate and extend Helda's data pipelines as we onboard healthcare organisations onto the platform. You will be the person who takes a new hospital's raw data — however it arrives — and gets it flowing cleanly into Supabase so the analytics platform can serve it.
Required Skills
Preferred Skills
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Responsibilities
- Lead the technical side of onboarding new healthcare organisations: receive their data exports, assess data quality, map their columns to Helda's canonical schemas, and configure their ingestion pipeline
- Handle the diversity of source formats: CSV files with inconsistent headers, Excel workbooks with multiple sheets, database dumps, EMR exports with vendor-specific quirks, and FHIR API responses
- Work with healthcare org contacts to understand their data: what each column represents, which fields are populated vs. sparse, what their date formats and currency conventions are
- Document each org's data profile: source system, delivery method, column mappings, known data quality issues, and any org-specific transformation rules
- Run the automated validation suite on every new data load — verify schema compliance, data type correctness, value ranges, and analytics compatibility
- Investigate and resolve validation failures: missing required columns, unexpected data types, out-of-range values, duplicate records, encoding issues
- Extend validation rules when new edge cases surface (e.g., a hospital that uses a different claim status taxonomy, a pharmacy with drug names in a non-standard format)
- Perform manual spot-checks on analytics output after loading new org data — verify that KPI calculations, trend charts, and LLM-generated analytics produce sensible results for the new dataset
- Monitor ingestion pipeline health across all active organisations: check for failed loads, stale data, schema drift, and data quality alerts
- Troubleshoot pipeline failures: parse errors, connection timeouts, rate limits on FHIR APIs, schema changes from source systems
- Run incremental data loads when orgs provide updated data (new months, corrections, backfills)
- Maintain pipeline configuration: connection credentials (rotated securely), schedule configurations, retry policies
- Write and maintain transformation scripts that handle org-specific data quirks: column renaming, date format conversion, currency normalization, categorical value standardisation
- Handle the column normalization that Helda requires
- Build and maintain mapping tables for categorical standardization
- Clean and deduplicate records where source data has quality issues, documenting all transformations applied
- Write data validation tests for new org schemas — verify that every column the analytics engine expects is present and correctly typed
- Write regression tests: after a data reload or schema change, confirm that existing analytics queries still produce correct results
- Run end-to-end tests after every onboarding
- Report and track data quality metrics per org: completeness, consistency, freshness
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