Senior Site Reliability Engineer for Fuse team at Bloomreach

Bloomreach is building the world’s premier agentic platform for personalization.We’re revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the entire customer journey.

• We're taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses.
• We’re making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey.
• We're designing the future of autonomous marketing, taking the work out of workflows, and reclaiming the creative, strategic, and customer-first work marketers were always meant to do.

And we're building all of that on the intelligence of a single AI engine — Loomi — so that personalization isn't only autonomous…it's also consistent.From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora.

Become a Senior SRE for Bloomreach!

Join the Fuse team — the team responsible for the item data management capabilities that connect Bloomreach Data Hub with Marketing, Search, Recommendations, and emerging Loomi agent use cases.

Fuse owns and evolves the systems behind Data Hub item collections: ingesting items data, transforming and validating it, managing data schemas and lifecycle, and distributing data reliably to downstream Bloomreach products. Item collections provide a unified source of data that can be used across all Bloomreach products.

Our current areas of focus include:

• Unified items data pipelines: processing records into structured items and keeping data synchronized with Marketing and Search destinations.
• Catalog APIs and lifecycle management: customer-facing and internal APIs, catalog creation and naming, schemas, destinations, migrations, and backward-compatible evolution.
• Scalable storage and indexing: operating and improving systems built on PostgreSQL, Bigtable, Elasticsearch, Solr.
• Reliable jobs execution: submission, queueing, execution, progress reporting, retries, cancellation, rate limiting, and operational tooling.
• Cross-product capabilities: catalog data triggers, multi-dimensional data support, custom item types, catalog data enrichment, recommendations, and semantic catalog profiles for agentic use cases.

As a Senior SRE, you will be the team’s reliability and operability leader. You will work alongside backend engineers, embedded QA, Product, and Engineering Management to make complex product-data systems observable, scalable, safe to release, and straightforward to operate.

Fuse embraces AI-assisted engineering. We expect engineers to use modern coding agents thoughtfully to accelerate investigation, development, testing, documentation, and operational work while retaining full ownership of correctness, security, and production outcomes.

Working from one of our Central European offices (Bratislava, Prague, or Brno), or remotely (Czechia, Slovakia) on a full-time basis, you’ll become a core part of the Engineering organization.

What challenge awaits you?

As a P3 Senior SRE at Bloomreach, you are an independent reliability professional who can turn ambiguous operational problems into measurable improvements and lead initiatives end-to-end with minimal day-to-day guidance.

Your challenge will be to make Fuse’s distributed data platform dependable across the complete data path:

customer or integration → Data Hub API → records and transformations → items → asynchronous jobs execution engine → storage and indexes → Marketing, Search, Recommendations, and Loomi consumers

Your responsibilities

a. Platform reliability and observability

• Own and improve the reliability posture of Fuse services, workers, APIs, queues, storage systems, and destination synchronization pipelines.
• Establish meaningful SLIs, SLOs, and error budgets for customer-facing APIs, asynchronous jobs, catalog data freshness, destination synchronization, and indexing.
• Build end-to-end observability across Data Hub item collections, from API request and job submission through processing, persistence, indexing, and downstream delivery.
• Ensure engineers can trace a workspace, item collection, catalog, or job across services without manually correlating disconnected logs and database records.
• Create and maintain actionable dashboards, alerts, and service health views using Grafana, Prometheus-compatible metrics, OpenTelemetry, PagerDuty, and GCP tooling.
• Detect missing, stalled, duplicated, or inconsistent processing before customers or downstream teams report it.
• Improve capacity planning and autoscaling using workload telemetry, queue depth, processing throughput, latency, memory usage, storage growth, and customer-level traffic patterns.
• Reduce noisy alerts and replace symptom-based monitoring with signals tied to customer impact.

b. Reliability of catalog storage and indexing

• Improve the availability, scalability, and operability of catalog data across PostgreSQL/Cloud SQL, Bigtable, Elasticsearch, GCS, Kafka, and related storage systems.
• Support catalog placement, routing, index lifecycle, shard management, safe migration, and recovery across multiple Elasticsearch clusters.
• Develop safeguards for full replacements, delta updates, deletions, schema changes, destination changes, and catalog reindexing.
• Define and automate data-consistency checks between source records, transformed items, job state, Bigtable, Elasticsearch, and downstream destinations.
• Help establish practical platform limits and quotas for catalog size, API traffic, job concurrency, queue depth, payload size, and expensive operations.
• Partner with engineers on performance testing for large catalogs and high-throughput customer workloads.

c. Infrastructure, deployments, and release safety

• Own and evolve Kubernetes configuration and operational infrastructure for Fuse components.
• Improve deployment automation, progressive rollout, rollback, and validation across development and production environments.
• Make coordinated releases safer when changes span app/app , Fuse workers, Kubernetes configuration, and PostgreSQL migrations.
• Automate operational procedures that currently depend on manual commands, one-off scripts, or specialist knowledge.
• Maintain CI/CD pipelines with tests, linters, dependency management, security checks, image publication, and release verification.
• Create reusable tooling for local development, ephemeral environments, end-to-end testing, load testing, and production diagnosis.
• Ensure runbooks remain executable and are validated through exercises rather than existing only as documentation.

d. Incident management and L3 support

• Participate in and help improve the Fuse L3/on-call rotation.
• Lead incident investigation, mitigation, stakeholder communication, and follow-up for Fuse-owned systems.
• Use logs, metrics, traces, database state, queue state, and Kubernetes signals to diagnose failures across distributed workflows.
• Build safe operational tools for common support activities such as job tracing, queue inspection, rate-limit diagnosis, catalog health checks, and index recovery.
• Facilitate blameless incident reviews and ensure resulting actions address root causes rather than only immediate symptoms.
• Improve the handoff between customer support, L2, Fuse L3, Infrastructure, and dependent engineering teams.
• Reduce recurring support demand by turning incident knowledge into safeguards, automation, tests, dashboards, and clear documentation.

e. Security, isolation, and compliance

• Help Fuse meet Bloomreach security and compliance requirements, including ISO and SOC 2 controls.
• Enforce least-privilege access, workload identity, service-level authentication and authorization, secret rotation, encryption, and auditability.
• Protect customer isolation across workspaces, item collections, projects, accounts, databases, indexes, buckets, and asynchronous jobs.
• Ensure operational tooling and incident procedures respect production-access restrictions and PII-handling requirements.
• Partner with engineering teams to make security controls observable and testable rather than relying on undocumented assumptions.

f. Reliability by design

• Participate early in the design of new Fuse capabilities so reliability, recovery, observability, limits, and operational ownership are defined before implementation.
• Review designs for failure modes, retry behavior, idempotency, backpressure, ordering, consistency, timeout handling, cancellation, and safe rollout.
• Clarify ownership boundaries and service contracts with teams including Campaigns, Data Pipeline, Integrations, Discovery, Recommendations, Infrastructure, Frontend, and QA.
• Help teams choose architectures that balance immediate delivery with long-term operability and cost.
• Coach engineers in production readiness, operational testing, debugging, and sustainable on-call practices.

Our tech stack

Primary languages: Go, Python, SQL
APIs and application: REST APIs, Python application monolith, Go workers and services, Java Infrastructure: GCP, Kubernetes/GKE, internal Kubernetes deployment tooling
Databases and storage: PostgreSQL/Cloud SQL, Bigtable, Elasticsearch, GCS, MongoDB, Redis Messaging and coordination: Kafka, ETCD, asynchronous job queues
Observability: Grafana, Prometheus-compatible metrics, OpenTelemetry, GCP Logging and Monitoring, PagerDuty CI/CD and collaboration: GitLab, Jira, Confluence
Testing: Go and Python unit/integration tests, API and end-to-end automation, performance testing
AI-assisted engineering: Claude Code, Cursor, Copilot, Gemini CLI, or comparable tools

You do not need to have used every technology listed. You should, however, have operated distributed production systems and be comfortable learning unfamiliar components while diagnosing real incidents.

Your qualifications

Professional experience

Impact

• You can show how your reliability work improved customer outcomes, engineering velocity, deployment confidence, or operational sustainability.
• You have introduced practices or tooling that changed how a team builds and operates production systems.
• You can define meaningful reliability measures and demonstrate improvement using data.

Ownership

• You embrace the you build it, you run it principle and remain accountable from design through production operation.
• You can lead ambiguous reliability initiatives without requiring a fully prescribed solution.
• You take incidents from detection through mitigation, root-cause analysis, and prevention.
• You are cost-aware and use telemetry, capacity planning, and architecture—not guesswork—to manage cloud spend.

Systematic approach

• You treat observability, limits, runbooks, rollback, idempotency, and recovery as part of the product design.
• You design for partial failure in distributed systems.
• You distinguish symptoms from root causes and prioritize systemic improvements over repeated manual intervention.
• You are comfortable working in systems where a single customer operation crosses multiple services, queues, databases, and team boundaries.

Data-driven engineering

• You use metrics, logs, traces, profiling, and workload data to form and validate hypotheses.
• You can turn operational telemetry into actionable feedback for developers and Product.
• You are comfortable analyzing throughput, latency, saturation, error rates, queue behavior, database performance, and storage growth.

Technical skills

• Strong hands-on experience operating services on Kubernetes in a major cloud environment, ideally GCP.
• Strong experience with observability and incident diagnosis for distributed systems.
• Experience with Go or Python; practical ability in both is a strong advantage.
• Experience operating at least one relational database, preferably PostgreSQL or Cloud SQL.
• Experience with one or more large-scale data or indexing systems such as Bigtable, Elasticsearch/OpenSearch, Kafka, GCS, or comparable technologies.
• Experience designing or operating asynchronous job-processing systems, queues, workers, and retry mechanisms.
• Experience with CI/CD, Infrastructure as Code, deployment automation, and safe database migrations.
• Understanding of API reliability, rate limiting, backpressure, idempotency, and multi-tenant isolation.
• Comfort participating in an on-call rotation and responding to production incidents.
• Ability to work effectively in a distributed, remote-first team.
• Practical use of AI coding tools to accelerate investigation and implementation without outsourcing engineering judgment.

Strongly preferred

• Experience operating catalog, product-data, ingestion, transformation, or indexing platforms.
• Experience with large Elasticsearch/OpenSearch clusters, shard management, index lifecycle, routing, or reindexing.
• Experience with Bigtable or another distributed wide-column database.
• Experience designing consistency validation across multiple storage or indexing systems.
• Experience with customer-facing data APIs, high-volume bulk ingestion, or full and incremental synchronization.
• Familiarity with product catalogs used by search, recommendations, marketing, or personalization systems.
• Experience coordinating reliability improvements across several engineer

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