feat(data): integrate 9 UK government data sources via Kestra

Adds a full data integration pipeline for enriching school profiles with
supplementary data from Ofsted, GIAS, EES, IDACI, and FBIT.

Backend:
- Bump SCHEMA_VERSION to 3; add 8 new DB tables (ofsted_inspections,
  ofsted_parent_view, school_census, admissions, sen_detail, phonics,
  school_deprivation, school_finance) plus GIAS columns on schools
- Expose all supplementary data via GET /api/schools/{urn}
- Enrich school list responses with ofsted_grade + ofsted_date

Integrator (new service):
- FastAPI HTTP microservice; Kestra calls POST /run/{source}
- 9 source modules: ofsted, gias, parent_view, census, admissions,
  sen_detail, phonics, idaci, finance
- 9 Kestra flow YAMLs with scheduled triggers and 3× retry

Frontend:
- SchoolRow: colour-coded Ofsted badge (Outstanding/Good/RI/Inadequate)
- SchoolDetailView: 7 new sections — Ofsted sub-judgements, Parent View
  survey bars, Admissions, Pupils & Inclusion / SEN, Phonics, Deprivation
  Context, Finances
- types.ts: 8 new interfaces + extended School/SchoolDetailsResponse

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/models.py

@@ -7,7 +7,7 @@ from datetime import datetime
 
 from sqlalchemy import (
     Column, Integer, String, Float, ForeignKey, Index, UniqueConstraint,
-    Text, Boolean, DateTime
+    Text, Boolean, DateTime, Date
 )
 from sqlalchemy.orm import relationship
 from .database import Base
@@ -38,7 +38,16 @@ class School(Base):
     # Geocoding (cached)
     latitude = Column(Float)
     longitude = Column(Float)
-    
+
+    # GIAS enrichment fields
+    website = Column(String(255))
+    headteacher_name = Column(String(200))
+    capacity = Column(Integer)
+    trust_name = Column(String(255))
+    trust_uid = Column(String(20))
+    gender = Column(String(20))        # Mixed / Girls / Boys
+    nursery_provision = Column(Boolean)
+
     # Relationships
     results = relationship("SchoolResult", back_populates="school", cascade="all, delete-orphan")
     
@@ -150,6 +159,169 @@ class SchemaVersion(Base):
         return f"<SchemaVersion(version={self.version}, migrated_at={self.migrated_at})>"
 
 
+# ---------------------------------------------------------------------------
+# Supplementary data tables (populated by the Kestra data integrator)
+# ---------------------------------------------------------------------------
+
+class OfstedInspection(Base):
+    """Latest Ofsted inspection judgement per school."""
+    __tablename__ = "ofsted_inspections"
+
+    urn = Column(Integer, primary_key=True)
+    inspection_date = Column(Date)
+    publication_date = Column(Date)
+    inspection_type = Column(String(100))   # Section 5 / Section 8 etc.
+    # 1=Outstanding 2=Good 3=Requires improvement 4=Inadequate
+    overall_effectiveness = Column(Integer)
+    quality_of_education = Column(Integer)
+    behaviour_attitudes = Column(Integer)
+    personal_development = Column(Integer)
+    leadership_management = Column(Integer)
+    early_years_provision = Column(Integer)   # nullable — not all schools
+    previous_overall = Column(Integer)        # for trend display
+
+    def __repr__(self):
+        return f"<OfstedInspection(urn={self.urn}, overall={self.overall_effectiveness})>"
+
+
+class OfstedParentView(Base):
+    """Ofsted Parent View survey — latest per school. 14 questions, % saying Yes."""
+    __tablename__ = "ofsted_parent_view"
+
+    urn = Column(Integer, primary_key=True)
+    survey_date = Column(Date)
+    total_responses = Column(Integer)
+    q_happy_pct = Column(Float)          # My child is happy at this school
+    q_safe_pct = Column(Float)           # My child feels safe at this school
+    q_bullying_pct = Column(Float)       # School deals with bullying well
+    q_communication_pct = Column(Float)  # School keeps me informed
+    q_progress_pct = Column(Float)       # My child does well / good progress
+    q_teaching_pct = Column(Float)       # Teaching is good
+    q_information_pct = Column(Float)    # I receive valuable info about progress
+    q_curriculum_pct = Column(Float)     # Broad range of subjects taught
+    q_future_pct = Column(Float)         # Prepares child well for the future
+    q_leadership_pct = Column(Float)     # Led and managed effectively
+    q_wellbeing_pct = Column(Float)      # Supports wider personal development
+    q_behaviour_pct = Column(Float)      # Pupils are well behaved
+    q_recommend_pct = Column(Float)      # I would recommend this school
+    q_sen_pct = Column(Float)            # Good information about child's SEN (where applicable)
+
+    def __repr__(self):
+        return f"<OfstedParentView(urn={self.urn}, responses={self.total_responses})>"
+
+
+class SchoolCensus(Base):
+    """Annual school census snapshot — class sizes and ethnicity breakdown."""
+    __tablename__ = "school_census"
+
+    urn = Column(Integer, primary_key=True)
+    year = Column(Integer, primary_key=True)
+    class_size_avg = Column(Float)
+    ethnicity_white_pct = Column(Float)
+    ethnicity_asian_pct = Column(Float)
+    ethnicity_black_pct = Column(Float)
+    ethnicity_mixed_pct = Column(Float)
+    ethnicity_other_pct = Column(Float)
+
+    __table_args__ = (
+        Index('ix_school_census_urn_year', 'urn', 'year'),
+    )
+
+    def __repr__(self):
+        return f"<SchoolCensus(urn={self.urn}, year={self.year})>"
+
+
+class SchoolAdmissions(Base):
+    """Annual admissions statistics per school."""
+    __tablename__ = "school_admissions"
+
+    urn = Column(Integer, primary_key=True)
+    year = Column(Integer, primary_key=True)
+    published_admission_number = Column(Integer)   # PAN
+    total_applications = Column(Integer)
+    first_preference_offers_pct = Column(Float)    # % receiving 1st choice
+    oversubscribed = Column(Boolean)
+
+    __table_args__ = (
+        Index('ix_school_admissions_urn_year', 'urn', 'year'),
+    )
+
+    def __repr__(self):
+        return f"<SchoolAdmissions(urn={self.urn}, year={self.year})>"
+
+
+class SenDetail(Base):
+    """SEN primary need type breakdown — more granular than school_results context fields."""
+    __tablename__ = "sen_detail"
+
+    urn = Column(Integer, primary_key=True)
+    year = Column(Integer, primary_key=True)
+    primary_need_speech_pct = Column(Float)    # SLCN
+    primary_need_autism_pct = Column(Float)    # ASD
+    primary_need_mld_pct = Column(Float)       # Moderate learning difficulty
+    primary_need_spld_pct = Column(Float)      # Specific learning difficulty (dyslexia etc.)
+    primary_need_semh_pct = Column(Float)      # Social, emotional, mental health
+    primary_need_physical_pct = Column(Float)  # Physical/sensory
+    primary_need_other_pct = Column(Float)
+
+    __table_args__ = (
+        Index('ix_sen_detail_urn_year', 'urn', 'year'),
+    )
+
+    def __repr__(self):
+        return f"<SenDetail(urn={self.urn}, year={self.year})>"
+
+
+class Phonics(Base):
+    """Phonics Screening Check pass rates."""
+    __tablename__ = "phonics"
+
+    urn = Column(Integer, primary_key=True)
+    year = Column(Integer, primary_key=True)
+    year1_phonics_pct = Column(Float)   # % reaching expected standard in Year 1
+    year2_phonics_pct = Column(Float)   # % reaching standard in Year 2 (re-takers)
+
+    __table_args__ = (
+        Index('ix_phonics_urn_year', 'urn', 'year'),
+    )
+
+    def __repr__(self):
+        return f"<Phonics(urn={self.urn}, year={self.year})>"
+
+
+class SchoolDeprivation(Base):
+    """IDACI deprivation index — derived via postcode → LSOA lookup."""
+    __tablename__ = "school_deprivation"
+
+    urn = Column(Integer, primary_key=True)
+    lsoa_code = Column(String(20))
+    idaci_score = Column(Float)    # 0–1, higher = more deprived
+    idaci_decile = Column(Integer) # 1 = most deprived, 10 = least deprived
+
+    def __repr__(self):
+        return f"<SchoolDeprivation(urn={self.urn}, decile={self.idaci_decile})>"
+
+
+class SchoolFinance(Base):
+    """FBIT financial benchmarking data."""
+    __tablename__ = "school_finance"
+
+    urn = Column(Integer, primary_key=True)
+    year = Column(Integer, primary_key=True)
+    per_pupil_spend = Column(Float)          # £ total expenditure per pupil
+    staff_cost_pct = Column(Float)           # % of budget on all staff
+    teacher_cost_pct = Column(Float)         # % on teachers specifically
+    support_staff_cost_pct = Column(Float)
+    premises_cost_pct = Column(Float)
+
+    __table_args__ = (
+        Index('ix_school_finance_urn_year', 'urn', 'year'),
+    )
+
+    def __repr__(self):
+        return f"<SchoolFinance(urn={self.urn}, year={self.year})>"
+
+
 # Mapping from CSV columns to model fields
 SCHOOL_FIELD_MAPPING = {
     'urn': 'urn',