Data Reliability Engineer II

The Data Reliability Engineering (PRE) team at Groupon ensures all organizational data powering financial reporting, revenue management, HR, marketing, and finance operates within defined service levels. The team has evolved into a software-first reliability engineering function managing hundreds of data pipelines across multiple verticals.

This SDE II position is a full-time role replacing a contract position, reflecting the team’s need for deep business understanding, direct stakeholder relationships, and production accountability. You will work alongside engineers in Bangalore, collaborating with a globally distributed team across the US, Brazil, and Europe.

• Own on-call rotations - triage and investigate pipeline failures across Google Cloud Composer (Airflow), Keboola, N8N, third-party-vendor Jobs with end-to-end accountability; perform deep root cause analysis to identify permanent fixes.
• Maintain SLO/SLA compliance across Tier 1, Tier 2 data assets; author blameless postmortems, incident runbooks, and handover documentation.

• Build automation tools and own internal products (Claude/Google ADK Agent, Prometheus query-exporter) as a software engineer; integrate automated data quality checks (schema validation, null-count, freshness) into Github Actions CI/CD pipelines.
• Manage and optimize ETL/ELT workflows (SQL/Python) across revenue, pricing, promotions, event datastreams and customer data platform; contribute to self-healing mechanisms with automated retries and dynamic scaling.

• Provision, configure, and upgrade Google Cloud Composer environments (Dev/Staging/Prod); manage CI/CD governance, secrets, access permissions, and cloud security posture for production infrastructure.
• Conduct capacity planning reviews for the data warehouse, forecasting scaling needs based on utilization trends.

• Instrument SLO boards in JSM to reduce paging fatigue; drive cost-tuning and performance initiatives; support the Data Unification initiative and broader data architecture goals.
• Serve as the communication bridge between technical failures and business stakeholders (Sales, Marketing, Finance, Revenue); partner on pipeline design reviews and contribute to quarterly Reliability Reviews.

• Scripting Python / Linux Proficiency - production scripting with usage (not complete reliance on) of AI-generated black-box code.
• Expert SQL - complex queries, large-table transactions (50M+ rows), execution plans, and lock prevention.
• Git Fluency - branching, stashing, cherry-picking, and hotfix workflows in a fast-moving sprint environment.
• Root Cause Analysis Autonomy - trace failures across multiple systems and layers, not just restart services.
• Production System Experience - exclusive production access with business-critical accountability; minimum 3 years (3.5–4 preferred).
• Cloud & Orchestration Platforms - hands-on with Google Cloud Composer/Airflow and GCP; DAG management and debugging essential.
• CI/CD & Automation Mindset - build automation tools and maintain CI/CD workflows; think like an SDE, not L1 support.

• BigQuery or equivalent (Redshift, Snowflake, Azure Data Factory) experience.
• Familiarity with AI coding tools (Cursor, Claude Code) and the critical thinking to avoid over-reliance.
• Experience with Airflow, Keboola or equivalent (Fivetran, Matillion etc) background in SRE, Platform, or Data Engineering.

• Vendor tooling: CDP, Keboola, Encore.js (learnable on the job); internal product maintenance (React/Next.js agents and dashboards).
• SLO governance frameworks, Error Budget policy design, and data lifecycle knowledge across Financial, Business, Customer, and Product verticals.

• Engineers who take ownership of systems and care about long-term reliability.
• People who value automation over manual work.
• Candidates who communicate clearly and collaborate well across teams.
• Individuals who are curious, practical, and comfortable working in production environments.