EPAM's Operational Intelligence practice is expanding from classic infrastructure and application observability into Data & AI Reliability Engineering. We are looking for a Data Reliability Engineer who will apply SRE principles to data pipelines and data platforms — moving our clients from reactive firefighting to proactive management of data quality, so that data stays accurate, complete, fresh and available end-to-end. This is an engineering role, not an L1/L2 support role: you will build detection, automation and prevention rather than sit in a 24/7 on-call rotation. You will work across client engagements and practice-level initiatives — reusable data quality frameworks, accelerators, and internal enablement. What You’ll Get A practice that is actively building a new capability — your work becomes the standard, not a copy of someone else's runbook Engineering focus without 24/7 on-call rotation Internal certification and enablement tracks (Databricks, AI/LLM, Data Observability learning path in Learn) Cross-client exposure: enterprise-scale retail, financial services and manufacturing accounts Responsibilities Design and implement end-to-end data observability across pipelines and data platforms, covering the core data quality pillars: freshness, volume, schema, completeness and accuracy Embed automated data quality checks into CI/CD pipelines and orchestrators (e.g., dbt tests, Great Expectations, native platform checks) Configure anomaly detection — including dynamic and ML-based thresholds — for data drift, volume anomalies and pipeline failures Define, measure and report Data SLIs, SLOs and error budgets together with business and data product stakeholders Perform root cause analysis by tracing data lineage upstream to the exact origin of a failure; drive problem management so incidents do not recur Design architectural guardrails for data pipelines: circuit breakers, dead-letter queues, retry and rollback mechanisms, self-healing patterns Implement monitoring and alerting as code (Terraform / GitOps) instead of manual UI configuration Reduce alert noise through event correlation, tagging standards and actionable alert design Build reusable data quality and observability frameworks, templates and accelerators adopted by multiple data product teams Lead post-incident reviews and translate learnings into systemic platform improvements Contribute to presales activities, client-facing assessments and internal training materials for the practice Requirements 4+ years in SRE, DevOps, Data Engineering or Data Platform Operations, with at least 1–2 years focused on data platforms or data pipelines Solid SRE fundamentals: Golden Signals, SLI/SLO definition and calculation, error budgets and burn rate, incident lifecycle, ITIL basics Strong SQL and practical Python for automation and validation scripting Hands-on production experience with at least one cloud platform: Azure, AWS or GCP Experience with at least one modern data platform and orchestration stack — Databricks (preferred), Snowflake, Spark, Airflow, Azure Data Factory, dbt Working knowledge of observability tooling: New Relic, Datadog, Splunk, Elastic Stack, Grafana / OpenTelemetry (any two or more) Infrastructure as Code with Terraform (Ansible is a plus) and CI/CD experience (Azure DevOps, GitLab CI, GitHub Actions) Experience with incident and change management tooling: ServiceNow, PagerDuty or equivalent Ability to troubleshoot complex distributed data issues under SLA pressure B2+ English — the role is client-facing and requires clear written and spoken technical communication Nice to have Databricks certification (Data Engineer Associate / Professional) or equivalent cloud data certification Hands-on experience with dedicated data observability platforms (Monte Carlo, Soda, Anomalo, Great Expectations) Data catalog and lineage tooling (Unity Catalog, OpenLineage, Collibra) FinOps: cloud, platform and telemetry cost optimization Exposure to AI/ML pipeline monitoring or AI Reliability Engineering Power BI or another BI layer, from a monitoring and reliability perspective Event correlation / AIOps experience (New Relic Decisions, IBM NOI, ServiceNow ITOM) Mentoring or team lead experience
