Hire Elastic Engineers

What Are Elastic Engineers & Why They Matter

Why Elastic Engineers Power Modern Search, Observability, and Security

Elastic engineers are specialized software professionals who architect, deploy, and optimize solutions using the Elastic Stack, a comprehensive platform encompassing Elasticsearch (distributed search and analytics engine), Kibana (data visualization), Logstash (data processing pipeline), and Beats (data shippers). Unlike generalist backend engineers, Elastic engineers possess deep expertise in distributed systems, search relevance algorithms, observability patterns, and security analytics. According to 6sense market intelligence, over 28,000 companies globally use Elasticsearch, including 50% of the Fortune 500, from Netflix and Uber handling massive-scale log analytics, to Walmart powering customer-facing search experiences.

For enterprises prioritizing operational visibility, security posture, or customer experience through search, Elastic engineers deliver strategic differentiation. They architect observability platforms ingesting millions of events per second, providing real-time insights into application performance, infrastructure health, and user behavior. They build SIEM (Security Information and Event Management) solutions detecting threats across endpoints, networks, and cloud environments, correlating billions of security events to identify breaches before damage occurs. They implement enterprise search powering everything from e-commerce product discovery to internal knowledge bases, delivering sub-100ms query responses across petabyte-scale datasets.

Market demand for Elastic expertise has accelerated as organizations abandon legacy monitoring tools (Splunk, proprietary SIEM platforms) and monolithic search engines for open, cloud-native architecture. According to ZipRecruiter salary data, Elastic engineers in the US earn $114,500–$187,500 annually (median $139,000), reflecting premium demand for specialized search and observability skills. The Elastic Stack's versatility, supporting search, observability, and security from unified infrastructure, makes engineers proficient across these domains highly valuable. Organizations migrating to cloud-first observability, implementing zero-trust security, or modernizing customer search require engineers who understand Elastic's distributed architecture, query DSL, index optimization, and operational best practices.

At Scrums.com, our Software Engineering Orchestration Platform (SEOP) provides access to pre-vetted Elastic engineers who bring production experience across search relevance tuning, multi-cluster observability deployments, and SIEM rule optimization. Whether you're centralizing log management, building real-time analytics, or migrating from legacy search infrastructure, our engineers deliver both technical implementation and architectural guidance.

Essential Skills Elastic Engineers Must Have

Core Technical Competencies for Production Elastic Deployments

When hiring Elastic engineers, proficiency extends far beyond running localhost Elasticsearch instances. Elite engineers demonstrate mastery across distributed systems, query optimization, operational resilience, and use-case-specific implementation patterns.

Elasticsearch Architecture & Cluster Management: Expert Elastic engineers understand distributed system fundamentals, shard allocation strategies, replication topology, cluster coordination, and split-brain prevention. They architect clusters for specific workloads: hot-warm-cold architectures for log retention policies, dedicated master nodes for cluster stability, coordinating-only nodes for query routing. They monitor critical cluster health metrics (heap usage, GC pauses, indexing throughput, search latency) and troubleshoot issues like unassigned shards, circuit breaker trips, and merge throttling. Production clusters processing billions of documents require engineers who can tune thread pools, optimize index settings, and manage capacity planning.

Query DSL & Search Relevance Tuning: Elasticsearch's query language (Query DSL) powers everything from simple keyword searches to complex boolean queries, fuzzy matching, geo-spatial filters, and aggregations. Expert engineers craft queries balancing relevance and performance, understanding when to use match queries vs term filters, boosting strategies, function score queries, and rescoring. They implement autocomplete with edge n-grams, faceted search with aggregations, and personalized ranking with learning-to-rank models. Search relevance tuning—analyzing clickthrough data, A/B testing scoring algorithms, and iterating query templates, requires both technical skill and product intuition.

Data Ingestion & Pipeline Engineering: Production Elastic deployments ingest data from hundreds of sources, application logs, infrastructure metrics, security events, business data. Engineers implement ingestion pipelines using Logstash (complex transformations, enrichment, filtering), Beats (lightweight shippers), or direct API integration. They parse unstructured log formats with grok patterns, enrich data with GeoIP lookups, normalize timestamps across time zones, and handle backpressure when ingestion exceeds indexing capacity. Pipeline reliability, retry logic, dead letter queues, monitoring ingestion lag, separates hobbyist from production-grade implementations.

Kibana Dashboarding & Visualization: Kibana transforms Elasticsearch data into actionable insights through dashboards, visualizations, and alerting. Expert engineers build executive dashboards aggregating business KPIs, operational dashboards tracking application performance, and security dashboards monitoring threat indicators. They leverage Kibana's visualization types (time series, geographic maps, network graphs, heatmaps), implement drill-down workflows, and configure role-based access controlling dashboard visibility. Advanced use cases include Canvas for pixel-perfect reporting and Lens for no-code visual analytics.

Observability & APM Implementation: Elastic Observability unifies logs, metrics, and traces for comprehensive system visibility. Engineers instrument applications with Elastic APM agents (Java, Node.js, Python, .NET), correlate traces across microservices, and build service maps visualizing dependencies. They configure anomaly detection identifying performance degradations, set up alerts triggering on SLO violations, and integrate with incident management tools (PagerDuty, Opsgenie). Understanding distributed tracing standards (OpenTelemetry, Jaeger) and observability patterns (RED metrics, USE method) ensures implementations deliver actionable insights.

Security Analytics & SIEM: Elastic Security provides SIEM, endpoint security, and threat hunting capabilities. Engineers configure detection rules based on MITRE ATT&CK framework, analyze security events from EDR platforms (CrowdStrike, Carbon Black), correlate network traffic with endpoint behavior, and investigate incidents using Kibana's security interface. They implement threat intelligence integration, automate response workflows, and build compliance reporting for frameworks like SOC 2, ISO 27001, and NIST. Production SIEM deployments require understanding security operations workflows, not just technical configuration.

Infrastructure as Code & Automation: Production Elastic infrastructure should be reproducible, version-controlled, and automated. Engineers provision clusters with Terraform or CloudFormation, configure Elasticsearch via Ansible playbooks, and deploy via CI/CD pipelines. They implement backup strategies (snapshot repositories in S3, Azure Blob, GCS), automate index lifecycle management (hot/warm/cold/delete policies), and script operational tasks. Kubernetes operators enable declarative Elastic deployments, but require understanding operator patterns and custom resource definitions.

At Scrums.com, our vetting process validates these competencies through architecture design assessments, query optimization challenges, and operational troubleshooting scenarios. Our Dedicated Teams and Staff Augmentation include Elastic engineers who've scaled clusters to hundreds of nodes, optimized search latency under load, and implemented enterprise observability platforms processing terabytes daily.

Where Elastic Engineers Deliver Measurable ROI

Real-World Applications Driving Operational Excellence

Elastic engineers create measurable business impact across observability, security, and search use cases. Here are four scenarios where specialized Elastic talent delivers competitive advantage:

Observability Consolidation Reducing Tool Sprawl

Enterprises often operate fragmented observability stacks, Splunk for logs, Datadog for metrics, New Relic for APM, proprietary tools for infrastructure monitoring. Each vendor charges per-GB ingested or per-host monitored, creating unpredictable costs scaling with data volume. Elastic engineers unify these capabilities on Elastic Observability, consolidating logs, metrics, and traces into single platform. A financial services company we supported migrated from Splunk (costing $2M+ annually for 10TB/day log ingestion) to Elastic Cloud, reducing observability costs by 60% while improving query performance. Engineers implemented hot-warm-cold architecture storing recent data on fast SSDs, historical data on object storage, dramatically reducing infrastructure spend. Unified observability also improved incident response: engineers correlate logs, metrics, and traces in single interface, reducing mean time to resolution (MTTR) by 40%.

SIEM Implementation Accelerating Threat Detection

Traditional SIEM platforms (Splunk ES, IBM QRadar) are expensive, complex to deploy, and difficult to scale. Security teams struggle correlating events across endpoints, networks, cloud environments, and applications, leading to alert fatigue (thousands of false positives) and missed threats. Elastic engineers implement Elastic Security SIEM ingesting security events from EDR platforms, firewalls, cloud logs, and application audit trails. They configure detection rules based on MITRE ATT&CK tactics, build threat hunting workflows, and integrate threat intelligence feeds. A healthcare provider we partnered with deployed Elastic SIEM processing 50 million security events daily, detecting and responding to ransomware lateral movement 75% faster than previous SIEM. Automated response playbooks triggered endpoint isolation, blocking attackers before encryption began. Cost savings exceeded $1M annually compared to legacy SIEM licensing.

E-commerce Search Optimization Driving Revenue

Online retailers lose revenue when customers can't find products, poor search relevance, slow query response, limited filtering create friction converting browsers into buyers. Elastic engineers rebuild e-commerce search using Elasticsearch, implementing relevance algorithms considering product popularity, personalization signals, and inventory availability. They optimize query performance through caching strategies, replica scaling, and query profiling. A fashion retailer we supported migrated from MySQL full-text search to Elasticsearch, improving search response time from 800ms to 40ms while supporting complex filters (size, color, price range, brand) and autocomplete suggestions. Conversion rate increased 18% attributed to improved search experience, translating to $12M+ annual revenue lift. Search relevance tuning, analyzing clickthrough rates, adjusting boosting factors, became ongoing optimization process rather than one-time migration.

Enterprise Search Reducing Information Silos

Knowledge workers waste hours searching across fragmented systems, SharePoint, Confluence, Google Drive, Jira, email, internal databases. Elastic engineers build unified enterprise search indexing content across these sources, providing single search interface with permissions-aware results (users only see content they're authorized to access). They implement connectors extracting content, enrich documents with metadata, and tune relevance for enterprise content patterns. A professional services firm we worked with deployed enterprise search indexing 500GB of documents, saving consultants an estimated 5 hours weekly previously spent searching for project artifacts, proposals, and best practices. At 200 consultants billing $250/hour, productivity gains exceeded $2.6M annually. Engineers implemented workplace search analytics identifying content gaps and improving information architecture.

These scenarios demonstrate why Elastic engineers hired through Scrums.com's delivery models accelerate value realization. Our engineers bring not just technical implementation skills but understanding of operational patterns, cost optimization strategies, and architec

Elastic Stack vs. Alternative Observability & Search Solutions

Making the Right Platform Decision for Your Use Case

Choosing between Elastic and alternatives depends on use case requirements, operational preferences, and cost structures. Here's how Elastic compares to major competitors:

Elastic vs. Splunk (Observability & SIEM)

Splunk pioneered log management and SIEM but charges per-GB ingested, creating unpredictable costs as data volumes grow. Organizations ingesting 10TB/day can face $2M+ annual Splunk licensing costs. Elastic offers usage-based pricing (compute + storage) dramatically reducing costs, especially when implementing hot-warm-cold architectures storing historical data cheaply. Performance-wise, Elasticsearch often outperforms Splunk on query latency for large-scale aggregations due to distributed architecture. However, Splunk's mature ecosystem (pre-built apps, extensive integrations) and established security operations workflows make it entrenched in enterprises. Choose Elastic when cost reduction is priority and teams have engineering resources customizing implementations; choose Splunk when mature pre-built solutions and vendor support outweigh cost concerns.

Elastic vs. Datadog/New Relic (Application Monitoring)

Datadog and New Relic excel at SaaS-delivered observability, agents install easily, dashboards work immediately, minimal operational overhead. They charge per-host or per-data-ingested with predictable monthly billing. Elastic requires more engineering effort (cluster management, dashboard building, integration configuration) but offers superior customization, data ownership, and cost efficiency at scale. Organizations with dedicated platform teams and complex observability requirements (custom integrations, advanced analytics, long-term retention) favor Elastic; startups and teams prioritizing speed-to-value over customization choose SaaS observability. Hybrid approaches are common: Datadog for metrics/APM, Elastic for logs and long-term storage.

Elastic vs. Apache Solr (Search)

Solr is Elasticsearch's main open-source search competitor, also built on Apache Lucene. Both provide full-text search, faceting, and distributed architecture. Elasticsearch differentiates through developer experience, JSON-based REST API, simpler cluster management, richer ecosystem (Kibana, Beats, Elastic Cloud). Solr requires more XML configuration and operational complexity. Performance is comparable for most workloads. Choose Elasticsearch for new projects unless organization has deep Solr expertise. Migration from Solr to Elastic is common modernization path.

Elastic vs. Algolia/Typesense (Hosted Search)

Algolia and Typesense provide fully-managed search APIs, upload data via API, integrate JavaScript library, get instant search functionality. They excel for customer-facing search in e-commerce, SaaS applications, and content sites. Pricing scales with records indexed and API calls. Elastic requires infrastructure management (even with Elastic Cloud) but offers unlimited customization, lower per-query costs at scale, and data ownership. Choose hosted search for rapid prototyping and straightforward use cases; choose Elastic when search powers core business logic, requires complex relevance tuning, or handles massive scale where API pricing becomes prohibitive.

When to Choose Elastic Stack

Elastic is optimal when your architecture needs:

• Unified Platform: Combining search, observability, and security on common infrastructure
• Cost Optimization: Reducing per-GB/per-host licensing costs through usage-based pricing
• Data Ownership: Maintaining control over data location, retention policies, and compliance
• Customization: Building workflows, integrations, and analytics beyond pre-built solutions
• Scale Requirements: Processing terabytes daily, retaining data for years, serving millions of queries

At Scrums.com, our consulting for Engineering Managers includes technology architecture assessments evaluating observability, security, and search requirements. We help you make informed platform decisions, whether Elastic adoption, hybrid strategies, or migrations from legacy tools.

What Elastic Engineers Cost (and Why Africa Delivers Value)

Understanding Elastic Engineer Compensation and TCO

Elastic engineers command premium salaries due to specialized distributed systems knowledge and cross-domain expertise (search, observability, security). Understanding true costs helps optimize hiring decisions.

US Market Salary Benchmarks

Elastic/Elasticsearch engineers in the US earn senior backend engineer salaries with premium for specialized platform knowledge:

• Mid-Level Elastic Engineer (3-5 years): $114,500 - $139,000 base salary
• Senior Elastic Engineer (6+ years): $139,000 - $164,500 base salary
• Principal/Staff Elastic Engineer: $164,500 - $187,500+ base salary
• Elastic Architect: $180,000 - $220,000+ base salary

These figures exclude benefits (30-40% additional), equity compensation, bonuses, or recruiting costs averaging $15,000-$25,000 per hire. Total cost of ownership for a senior US-based Elastic engineer exceeds $200,000 annually when factoring fully-loaded employment costs including infrastructure access (Elastic Cloud licenses, training, conferences).

UK and European Market Rates

UK and Western European markets show similar positioning for observability and search expertise:

• Senior Elastic Engineer (UK): £70,000 - £95,000
• Principal Elastic Engineer (UK): £95,000 - £120,000
• Continental Europe: €75,000 - €110,000 (senior level)

Total employment costs run 35-45% higher than base salaries when including statutory benefits, taxes, and overhead.

The Africa Advantage: 40-60% Cost Savings Without Compromise

Scrums.com's African engineering talent delivers world-class Elastic Stack expertise at 40-60% lower total cost compared to US or Western European hiring. Our engineers in South Africa, Kenya, Nigeria, and Egypt hold Elastic Certified Engineer credentials, work with the same enterprise clients, and deliver to the same production standards, but regional market economics enable dramatic cost efficiency.

Total Cost of Ownership Comparison (Senior Elastic Engineer):

• US In-House: $200,000+/year (salary + benefits + overhead)
• UK In-House: £95,000/year (~$120,000)
• Scrums.com Africa-Based: $85,000 - $110,000/year as a broader, rough example. With Scrums.com, subscriptions are monthly or annually and hiring is flexible to when, and for how long, you need an engineer.

Beyond Direct Cost: Hidden Hiring Expenses

In-house Elastic hiring carries substantial hidden costs:

• Recruiting: $15,000 - $25,000 per hire (specialized recruiters for niche skills, opportunity cost of unfilled roles)
• Onboarding: 4-6 months to full Elastic productivity (learning company systems, domain knowledge, cluster-specific configurations)
• Training & Certification: Elastic Certified Engineer training ($3,000-$5,000), conferences (ElasticON), ongoing education
• Infrastructure Costs: Development clusters, Elastic Cloud licenses, testing environments
• Turnover Risk: Average DevOps/platform engineer tenure 2-3 years; replacement costs equal 9-12 months salary

Scrums.com eliminates these costs through pre-vetted, certified talent, managed services, and flexible scaling. Deploy Elastic Certified Engineers in under 21 days, scale teams monthly, and maintain quality without recruitment overhead.

Strategic Sourcing Without Quality Compromise

Cost savings mean nothing without delivery excellence. Our engineers bring:

• Elastic Certified Engineer credentials (official certification validating expertise)
• Production Elastic experience (multi-cluster deployments, petabyte-scale implementations)
• Cross-domain knowledge (search, observability, security use cases)
• English fluency and timezone overlap (UK/EMEA/US East Coast alignment)
• SEOP visibility for delivery transparency

Whether you need Staff Augmentation, Dedicated Teams, or full Product Development as a Service, Scrums.com delivers enterprise-grade Elastic engineering at unmatched value.

Elastic Stack Performance Optimization & Scaling Patterns

Engineering for Query Latency, Ingestion Throughput, and Cost Efficiency

Elastic's flexibility enables everything from laptop development to petabyte-scale deployments, but production performance requires architectural discipline. Expert Elastic engineers implement patterns ensuring speed, resilience, and cost optimization:

Index Design & Shard Optimization

Poor index design is the most common Elastic performance bottleneck. Engineers balance shard count (too few = hot spots, too many = overhead), shard size (target 10-50GB per shard), and replica count (more replicas = better query throughput, higher storage cost). Time-series data (logs, metrics) benefits from index-per-day or index-per-week patterns with automated lifecycle management, hot data on fast SSDs, warm data on standard storage, cold data on object storage, automatic deletion after retention period. Document modeling decisions, nested objects vs parent-child relationships, field mappings (keyword vs text), dynamic mapping control, significantly impact query performance and storage efficiency.

Query Performance & Caching Strategies

Sub-100ms query latency requires understanding Elasticsearch's caching layers and query execution. Filter context queries (exact matches, range filters) cache aggressively via filter cache; query context (relevance scoring) cannot cache. Engineers structure queries using filter context where possible, implementing bool queries separating filters from scoring. Shard request cache stores aggregation results, dramatically speeding repeated dashboard queries. Coordinating node query cache reduces parsing overhead. For user-facing search, engineers implement application-level caching (Redis, Memcached) for popular queries, reducing Elasticsearch load by 70-80%. Query profiling identifies slow operations (expensive aggregations, wildcard queries, large result sets) for optimization.

Ingestion Pipeline Scaling & Backpressure Handling

Production systems must handle ingestion spikes without data loss or performance degradation. Engineers implement backpressure handling, when Elasticsearch indexing cannot keep pace with incoming data, buffers (Logstash persistent queues, Kafka topics, Redis queues) absorb spikes without dropping events. Bulk indexing batches documents (1,000-5,000 per bulk request) reducing overhead. Ingestion pipelines run on dedicated nodes preventing search workload interference. For massive scale, engineers implement pre-processing layers (Kafka Streams, Flink) aggregating or filtering before Elasticsearch ingestion—reducing storage costs by 60% while maintaining analytical value.

Multi-Cluster Architecture & Cross-Cluster Search

Organizations operating globally or with regulatory requirements often deploy regional Elastic clusters. Engineers implement cross-cluster search enabling single query across multiple clusters, users query unified interface, Elasticsearch federates requests across clusters. This supports data sovereignty requirements (EU data stays in EU cluster, US data in US cluster) while maintaining user experience. Multi-cluster architectures also isolate failure domains, observability cluster issues don't impact security cluster. However, cross-cluster search has limitations (no writes, performance overhead) requiring careful architecture decisions.

Machine Learning for Anomaly Detection

Elastic's machine learning capabilities detect anomalies in time-series data without manual threshold configuration. Engineers train models on historical patterns (request latency, error rates, resource utilization), then ML algorithms automatically identify deviations, traffic spikes, performance degradations, security anomalies. For observability, this replaces hundreds of static threshold alerts with intelligent detection adapting to normal behavior patterns. Implementation requires understanding model types (metric analysis, population analysis, categorization), job configuration (bucket span, detectors, influencers), and alert integration. Poorly configured ML jobs generate false positives or miss real issues, requiring iterative tuning.

Security & Access Control Patterns

Production Elastic deployments require defense-in-depth security. Engineers implement authentication (SAML, LDAP, Active Directory integration), role-based access control (index-level, document-level, field-level permissions), audit logging tracking all cluster access, and encryption (TLS transport, encryption at rest). Document-level security enables multi-tenant architectures, multiple teams share cluster but cannot access each other's data. Field-level security redacts sensitive fields (PII, credentials) from query results based on user role. Security implementation requires understanding both Elasticsearch security features and organizational access patterns.

Scrums.com's Elastic engineers bring production performance optimization experience, not just theoretical knowledge. They've tuned clusters serving billions of queries, architected ingestion pipelines processing terabytes daily, and implemented security controls for regulated industries.