Senior Site Reliability Engineer for Datacraft team

Become a Senior SRE for Bloomreach!

Join the newly form Datacraft team — the team building the next-generation data platform for Bloomreach Engagement. Datacraft owns three interconnected domains:

• Data Warehouses (~60%) — making Bloomreach data first-class in customer DWHs (Snowflake, BigQuery, Databricks). The strategic goal for 2026–27 is to use DWHs to exponentially accelerate data adoption.
• Loomi Analytics Agent (~20%) — evolving Loomi Analytics into an agentic analytics assistant that can explore data across systems, explain insights, and act on them.
• Dashboards & Analytics Stack (~20%) — moving Engagement reporting onto DWH-backed, modern analytics stacks (semantic layers, headless BI tools).

As a Senior SRE, you will be the reliability backbone of this AI-first data team. Your work will directly impact the deployments, pipelines, reliability, and observability of pipelines and services that hundreds of enterprise customers depend on — from data exports into Databricks and BigQuery, to the  AI agent Loomi uses to surface insights.

Datacraft is an AI-first team. We believe code is a commodity and expect every engineer to fluently use coding agents (e.g., Cursor, Claude Code, Copilot, Gemini CLI) as a core part of their daily workflow. The ability to leverage AI tooling to accelerate development, prototyping, and problem-solving is not optional — it's foundational. Working in one of our Central European offices (Bratislava, Praha, Brno) or from home on a full-time basis, you'll become a core part of the Engineering team.

What challenge awaits you?

As a P3 (Senior) SRE at Bloomreach, you are an independent professional — expert in reliability engineering, able to decompose objectives into actionable infrastructure improvements, and lead initiatives end-to-end with minimal day-to-day guidance.

We need you to build and operate an ecosystem where data engineers can safely and efficiently develop, debug, and operate data-intensive jobs and services — spanning Kafka ingest pipelines, Iceberg data lakes, multi-DWH exports, Databricks deployment and orchestration (Airflow / Cloud Composer), and agentic AI workloads.

Your responsibilities

a. Platform reliability & observability

• Build and maintain the reliability ecosystem where engineers can safely develop, debug, and operate DataCraft services running on GCP and Kubernetes (DataProc, Cloud Composer, BigQuery, Snowflake/Databricks connectors).
• Ensure end-to-end observability across the full data platform — from Kafka ingest through GCS/Iceberg staging, Airflow orchestration, to Databricks and BigQuery destinations — enabling the team to catch missing loads, SLA breaches, and data drifts before customers notice, or costs drift.
• Drive scalability so services can scale vertically and horizontally based on operational and telemetric data (OpenTelemetry, Prometheus, Victoria Metrics).
• Maintain team health dashboards and alerting (Grafana, PagerDuty, Sentry).

b. Infrastructure as Code & deployments

• Own and evolve Terraform-based infrastructure for DataCraft services.
• Automate deployments, instance setup, and operational runbooks to eliminate manual/semi-manual steps.
• Maintain CI/CD pipelines (GitLab) with linters, security scans, and code quality checks, AI code reviews, enabling engineers to produce high-quality MRs.

c. Security & compliance

• Help the team fulfill security requirements for ISO and SOC2 audits by enforcing security principles: key distribution, key rotation, authorization & authentication at the service level, data encryption in transit, data isolation, resource limitations, and audit logs.
• Ensure data access controls are properly enforced across multi-DWH environments (BigQuery, Snowflake, Databricks).

d. Incident management & L3 support

• Participate in and drive L3 on-call rotation and incident resolution for DataCraft services.
• Contribute tooling for debugging, troubleshooting, and performance testing of data pipelines and orchestration layers.
• Use telemetry data and distributed tracing to navigate complex, distributed service architectures.

e. Agentic platform reliability

• Ensure reliability and observability of the Loomi Analytics Agent data infrastructure — LLM API gateway performance, MCP server health, and evaluation pipeline availability.
• Monitor and alert on data quality issues that could introduce inconsistencies or hallucinations in Loomi's responses — making the agent's data access patterns reliable and debuggable.

Our tech stack

Languages: Python (primary), Go, SQL Messaging & streaming: Apache Kafka Storage & databases: Databricks, BigQuery, Apache Iceberg, GCS, Mongo, Redis Data processing & orchestration: Apache Spark, DataFlow, Airflow / Cloud Composer Infrastructure: GCP, Kubernetes, Terraform AI / Agentic: LLM APIs, MCP, agent orchestration frameworks Observability: Grafana, Prometheus, Victoria Metrics, PagerDuty, Sentry, OpenTelemetry CI/CD & tooling: GitLab, Jira, Confluence AI coding agents: Cursor, Claude Code

Your qualifications

Professional experience

Impact

• You can articulate how your contributions transformed the way engineers work and fostered a strong SRE/DevOps culture.
• You can demonstrate how impactful reliability work connects to business success and customer outcomes.

Ownership

• You embrace the you build it, you run it principle — you love owning what you ship.
• You are cost-aware: effective vertical and horizontal autoscaling and detailed telemetry insights are how you demonstrate mindfulness of cloud spend.

Systematic approach

• Infrastructure as Code is the only thing that brings stability into chaos
• You design for failure: SLOs, error budgets, and runbooks are first-class artifacts, not afterthoughts.

Data-driven

• You use telemetry and metrics to give engineers actionable feedback on how applications and services behave.
• You can navigate complex data platform architectures using distributed tracing and debugging.

Technical skills

• Solid hands-on experience with GCP (BigQuery, DataProc, Cloud Composer, GCS) and Kubernetes.
• Experience with Python; Go is a strong advantage.
• Familiarity with data pipeline technologies (Kafka, Airflow/Cloud Composer, Spark, Iceberg) — you don't need to write ETL code, but you need to operate it reliably and know when something is wrong.
• Fluent use of AI coding agents (Cursor, Claude Code, Copilot, Gemini CLI, or similar) — you already use these tools daily to accelerate work.
• Comfortable with on-call rotation and 24/7 incident response.
• Remote-first mindset — you know how to be effective in distributed teams.
• You are able to learn and adapt — essential when exploring new tech or navigating our growing codebase.

Strongly preferred

• Experience operating single-DWH environments (Snowflak, Databricks or BigQuery).
• Familiarity with agentic/LLM workloads — API reliability, latency SLOs, trace observability for AI systems.
• Experience with open table formats (Iceberg, Delta Lake) in production environments.
• Exposure to data security and compliance in the context of customer-facing DWH integrations (consent, data retention, PII handling).

Personal qualities

• Ownership & accountability — you take issues from detection through to resolution and follow-up prevention.
• Systematic thinking — you identify root causes, not symptoms, and document your findings so the team learns.
• Collaboration & communication — you explain trade-offs and constraints clearly to both engineers and non-engineers.
• Bias for reliability — operational excellence (SLOs, oncall friendliness, proactive alerting) is not a chore, it's your craft.
• Continuous improvement mindset — you are comfortable iterating, revisiting assumptions, and improving incrementally.
• Comfortable operating remote-first in a distributed team across Central Europe.

Your success story

In 30 days:

• Get to know the DataCraft team, the company, and the most important processes.
• Set up your local and GCP development environment and complete the Engagement engineering onboarding.
• Understand the current state of DataCraft services: pipelines, orchestration, observability gaps, and on-call runbooks.

In 90 days:

• Start contributing to the L3 on-call rotation, handling incidents, troubleshooting, and debugging — which will sharpen your understanding of the platform and surface fresh improvement ideas.
• Deliver your first meaningful reliability improvement: an observability enhancement, a deployment automation, or an SLO definition for a key DataCraft service.

In 180 days:

• Own the reliability posture of at least one DataCraft domain end-to-end — able to independently design, operate, and continuously improve it.
• Drive measurable improvements in MTTR, alert signal-to-noise ratio, or deployment confidence across the team.
• Be a trusted reliability partner in architecture discussions — your input shapes how new DataCraft services are designed for operability from day one.

#LI-KP1