Hiring to Build Search Functionality: The Complete Guide

What Success Looks Like

Before hiring, understand what successful search implementation means for your users and business. Search has unique success metrics beyond traditional engineering goals.

Signs You Made the Right Hires

User Experience Quality

• Search results feel relevant—users find what they need in the first few results
• Autocomplete suggestions are helpful and responsive (<100ms)
• Typo tolerance catches common mistakes without returning irrelevant results
• Facets and filters help users narrow results intuitively
• Zero-result pages are rare and handled gracefully with suggestions
• Search works consistently across mobile and desktop

Technical Execution

• Query latency stays under 200ms at the 95th percentile
• Indexing keeps pace with content updates (near real-time for most cases)
• Search handles traffic spikes without degradation
• Relevance tuning is data-driven, not guesswork
• Monitoring reveals search quality issues before users complain
• Infrastructure costs scale predictably with usage

Business Impact

• Search-driven conversions or engagement meet targets
• User search behavior informs product decisions
• A/B testing enables continuous relevance improvement
• Search analytics reveal content gaps and user intent patterns

Red Flags in Implementation

Watch for these warning signs:

• Users immediately re-search with different terms (relevance problem)
• High bounce rates from search results pages
• Autocomplete suggestions feel random or unhelpful
• Exact-match searches returning no results
• Latency increasing as index grows
• No visibility into what users are searching for
• Search team can't explain why certain results rank higher

Technology Decisions: Build vs Buy

Your technology choice is the most important decision before hiring. It fundamentally changes who you need.

Managed Services (Algolia, Meilisearch Cloud, Elastic Cloud)

Choose managed services when:

1. Speed-to-market matters — Launch search in days, not months
2. Team is small — No dedicated search or infrastructure engineers
3. Search isn't your differentiator — It's a feature, not the product
4. Budget allows — $500-5000/month is acceptable
5. Scale is moderate — Under 10M records, under 100M searches/month

What you hire for: Backend developers who can integrate APIs, tune relevance, and build search UI. No search specialists needed.

Trade-offs:

• Vendor lock-in and pricing at scale
• Limited customization for edge cases
• Dependency on third-party availability
• May outgrow as data and queries scale

Self-Hosted (Elasticsearch, OpenSearch, Typesense)

Choose self-hosted when:

1. Search is your product — Core functionality requires deep control
2. Scale exceeds managed service limits — 100M+ records or 1B+ searches/month
3. Cost optimization is critical — Managed service pricing prohibitive at scale
4. Custom requirements exist — Unique ranking algorithms, specialized analyzers
5. Data sovereignty matters — Regulations require self-hosted infrastructure

What you hire for: Search engineers with Elasticsearch expertise, distributed systems knowledge, and relevance engineering skills. This is specialized work.

Trade-offs:

• Significant engineering investment (3-6 months for production-ready)
• Operational complexity (cluster management, upgrades, monitoring)
• Need specialized expertise that's expensive and hard to find
• Must build supporting infrastructure (monitoring, alerting, tooling)

The Hybrid Path

Many companies evolve through stages:

Stage 1: Managed service (Algolia/Meilisearch) — Ship quickly, validate product
Stage 2: Optimization — Tune relevance, add personalization with managed service
Stage 3: Evaluation — When costs or limitations hurt, evaluate self-hosted
Stage 4: Migration (if needed) — Move to Elasticsearch with dedicated team

This path lets you hire for current needs rather than anticipated future ones.

Roles You'll Need

The roles depend entirely on your technology choice.

For Managed Service Approach

Backend Developer with Search Experience

You don't need search specialists. Any solid backend developer can integrate Algolia or Meilisearch.

Core responsibilities:

• Integrate search service APIs with your application
• Design indexing strategy and data pipelines
• Configure relevance settings and ranking rules
• Build search UI components (autocomplete, facets, results)
• Implement analytics and A/B testing for search
• Optimize for cost and performance within service limits

Required skills:

• Strong backend development (Node.js, Python, Go, etc.)
• API integration experience
• Basic understanding of search concepts (indexing, relevance, analyzers)
• Frontend basics for search UI (or collaboration with frontend team)
• Data pipeline experience for keeping indexes synchronized

What to assess:

• Have they integrated a search service before?
• Can they explain basic relevance concepts?
• How would they handle keeping search index in sync with database?

For Self-Hosted Elasticsearch

Search Engineer / Elasticsearch Developer

This is specialized work requiring specific expertise.

Core responsibilities:

• Design and implement Elasticsearch cluster architecture
• Build custom analyzers, tokenizers, and filters for your domain
• Develop query DSL for complex search requirements
• Implement relevance tuning and ranking algorithms
• Handle index management, reindexing, and migrations
• Build monitoring and alerting for search infrastructure
• Optimize for query latency and indexing throughput

Required skills:

• Deep Elasticsearch expertise (3+ years production experience)
• Distributed systems understanding (sharding, replication, consistency)
• Query optimization and performance tuning
• Text analysis and NLP basics (stemming, synonyms, language detection)
• JVM tuning and Java fundamentals (Elasticsearch is Java-based)
• Infrastructure skills (cluster management, capacity planning)

What to assess:

• Can they design a sharding strategy for your data?
• How would they handle a relevance problem where obvious results rank low?
• Experience with cluster operations (upgrades, reindexing, disaster recovery)?
• Understanding of Elasticsearch query types and when to use each?

For Advanced Search (Semantic, ML-Powered)

Relevance Engineer / Search ML Engineer

Only needed for sophisticated search with machine learning.

Core responsibilities:

• Implement learning-to-rank models
• Build semantic/vector search with embeddings
• Design personalization and recommendation algorithms
• A/B test relevance improvements rigorously
• Analyze search logs to identify improvement opportunities

Required skills:

• Machine learning fundamentals
• NLP and text embeddings (sentence transformers, OpenAI embeddings)
• Search relevance metrics and evaluation
• Statistical analysis for A/B testing
• Elasticsearch or similar at advanced level

This role is rare and expensive. Only hire if you've exhausted simpler approaches and have data proving ML will improve outcomes.

Team Structure Options

Option 1: Feature Team with Managed Service (Most Common)

How it works: Search is one feature among many. Backend developers integrate the managed service and tune relevance. No dedicated search expertise.

Option 2: Dedicated Search Team with Elasticsearch

How it works: Dedicated team owns search infrastructure and relevance. Platform approach where other teams consume search as a service.

Option 3: Embedded Specialist

How it works: One experienced search engineer provides guidance across product teams. Good for scaling expertise without large dedicated team.

Common Pitfalls

1. Choosing Elasticsearch Before You Need It

The mistake: Defaulting to Elasticsearch because "we'll need it eventually" or "it's what big companies use."

What happens: 6 months building infrastructure, ongoing operational burden, and a search feature that Algolia would have shipped in 2 weeks. Your product still has 10,000 users.

Better approach: Start with managed services. Migration to Elasticsearch is well-documented when you genuinely need it. Most companies never do. Algolia handles 100M+ searches/month for enterprises.

2. Underestimating Relevance Engineering

The mistake: Treating relevance as a configuration problem that's "basically done" once search returns results.

What happens: Users search for "running shoes" and get dress shoes. Synonyms, typos, and natural language variations all fail. Search "works" but users don't find what they need.

Better approach: Relevance is ongoing work:

• Analyze search logs to understand user intent
• Configure synonyms and phrase matching
• Tune boost factors for important fields
• A/B test changes rigorously
• Build feedback loops from user behavior

Allocate ongoing engineering time for relevance improvement, not just initial implementation.

3. Ignoring Index Synchronization

The mistake: Building search without a robust strategy for keeping the index synchronized with the source database.

What happens: Products get updated but search shows stale data. New items don't appear in search. Deleted items still show up. Users lose trust in search accuracy.

Better approach: Design sync strategy from day one:

• Event-driven updates (database triggers, message queues)
• Full reindex capability for disaster recovery
• Monitoring for sync lag and failures
• Handling deletions explicitly
• Testing sync under load and failure conditions

4. Hiring Search Specialists Too Early

The mistake: Hiring an "Elasticsearch expert" before you've validated search requirements.

What happens: Expensive specialized hire builds complex infrastructure for a problem a managed service solves trivially. They get bored because there's no real search challenge. They leave.

Better approach: Start with backend developers and managed services. Only hire search specialists when you've genuinely outgrown simpler approaches—typically when you have specific Elasticsearch requirements that can't be met otherwise.

5. No Search Analytics

The mistake: Building search without instrumentation to understand how users search and what they find (or don't).

What happens: No data on what users search for, which searches return no results, or whether users click results. Can't measure search quality or identify improvement opportunities.

Better approach: Instrument from day one:

• Log all queries (with privacy considerations)
• Track clicks on search results (click-through rate by position)
• Monitor zero-result queries
• Measure search-to-conversion funnel
• Alert on search quality degradation

6. Over-Optimizing Before Understanding User Needs

The mistake: Building sophisticated features (semantic search, personalization, ML ranking) before basic search works well.

What happens: Complex infrastructure for advanced features, but basic keyword search is still broken. Users can't find exact matches while the team works on AI-powered recommendations.

Better approach: Master the basics first:

1. Exact match and keyword search working reliably
2. Typo tolerance and autocomplete
3. Faceted filtering
4. Synonyms and phrase matching
5. Then consider advanced features if data shows they'd help

Budget Planning

Team Costs (US Market, 2026)

Managed Services Approach

2-person team: $350K-440K/year total comp

Self-Hosted Elasticsearch Approach

3-person team: $580K-750K/year total comp

*Total comp includes equity, benefits, and employer costs (~20-30% overhead)

Infrastructure Costs

Cost Optimization Strategies

Evaluate total cost of ownership — Algolia at $3,000/month is cheaper than 1 engineer-month maintaining Elasticsearch.

Right-size your approach — Most companies don't need Elasticsearch. Don't pay the complexity tax for features you won't use.

Optimize index size — Only index searchable fields. Large indexes cost more in both managed and self-hosted.

Cache common queries — 80% of searches often come from 20% of queries. Caching reduces costs significantly.