The role the Data team plays at Agoda is critical as business users, product managers, engineers, and many others rely on us to empower their decision making. We are equally dedicated to our customers by improving their search experience with faster results and protecting them from any fraudulent activities. Data is interesting only when you have enough of it, and we have plenty. This is what drives up the challenge as part of the Data department, but also the reward.
The Opportunity:
As a Senior/Staff Data Engineer in CEG (Customer Experience Group), you will help design, build, and maintain the data and software foundations that power how CEG Business users serves customers and agents across all contact channels. You will work closely with Product, Operations, Analytics, Operations Efficiency, WFM, DevOps and engineering teams to deliver reliable data pipelines, scalable models, internal tools, and clear insights that improve customer experience and operational efficiency.
You are comfortable switching between engineering work (pipelines, data models, internal services, tooling, optimization) and analytics work (business questions, dashboards, deep‑dive analysis). You can talk to both engineers and business stakeholders in simple, clear language. At the Staff level, you will also shape CEG’s data and tooling strategy, influence cross‑team architecture, and raise the overall engineering and data bar for the team.
What You’ll Do:
Data Engineering
Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and serve data from CEG systems (case management, telephony, chat, bots, QA tools, WFM, CRM) and third‑party tools and logs.
Develop and optimize data models (e.g., warehouse tables, marts, views) that power CEG reporting, monitoring, forecasting, QA, and experimentation.
Ensure data quality, reliability, and observability:
Implement validation checks, anomaly detection, and monitoring for key CEG datasets and metrics.
Work with stakeholders to define and enforce data definitions, SLAs, and ownership for critical tables and metrics.
Improve performance and cost efficiency of data jobs and queries (e.g., partitioning, indexing, query tuning, storage format optimization) for high‑volume CEG data (calls, chats, emails, cases, events).
Collaborate with data platform and engineering teams to standardize tooling and best practices (e.g., version control, CI/CD for data and services, code review, documentation, runbooks).
Data Tools & Product Development
Design and build data tools and internal products for CEG (e.g., self‑service analytics, case monitoring dashboards, alerting systems, investigator tools, QA/review tools, agent performance views).
Use languages such as Python, Java, Golang, or JavaScript/TypeScript to implement APIs, data services, and lightweight UIs that expose data in a usable way to agents, managers, and operations teams.
Work closely with CEG product managers and operations to translate operational pain points into concrete data tools, from problem framing to delivery and iteration.
Apply solid software engineering practices—testing, code reviews, CI/CD, observability—to data products so they are reliable, maintainable, and easy to extend.
At Staff level, define and drive the roadmap for key CEG data tools and platforms, ensuring reuse across regions, lines of business, and channels.
Data Analytics & Business Impact
Partner with CEG product and business teams to translate questions into data problems and design clear analytic approaches.
Build and maintain dashboards and reports (e.g., contact volumes, handling time, quality, customer outcomes, agent performance, spot alerts) that provide reliable metrics and self‑service access to data.
Perform deep‑dive analysis to understand trends in contact patterns, customer issues, agent performance, and operational efficiency; identify root causes and propose practical, data‑driven recommendations.
Define and maintain metrics and KPIs (e.g., CSAT, NPS, SLA, AHT, FCR, quality scores), ensuring consistent definitions across CEG teams and tools.
Communicate findings in simple, business‑friendly language, including clear implications and recommended next steps.
Stakeholder & Team Collaboration
Act as a trusted data partner for CEG stakeholders (operations leaders, product managers, WFM, QA, training, policy, and regional leadership).
Work with other data engineers, analysts, and scientists to align on data standards, reusable components, and shared datasets for CEG.
Mentor junior and mid‑level team members on data engineering, analytics, and software engineering best practices.
Contribute to continuous improvement of team workflows (code reviews, testing, documentation, runbooks, knowledge sharing, incident reviews).
At Staff level, influence cross‑team architecture and ways of working, making sure CEG’s data ecosystem scales with business growth.
Required Qualifications:
8+ years working as a Data Engineer, Analytics Engineer, or Data Analyst in a data‑driven environment (Staff level typically 10+ or equivalent scope/impact).
Strong SQL skills (complex joins, window functions, aggregation, query optimization).
Experience with at least one major data warehouse / big data technology (e.g., BigQuery, Snowflake, Redshift, Hive, Spark, Vertica, StarRocks, or similar).
Solid experience with ETL/ELT tools or orchestration frameworks (e.g., Airflow, dbt, internal frameworks).
Proficiency in at least one programming language for data and services (e.g., Python, Java, Golang, Kotlin, or JavaScript/TypeScript).
Experience building production‑grade data pipelines and/or data services with attention to quality, monitoring, and maintainability.
Experience with BI / visualization tools (e.g., Tableau, Power BI, Superset, Metabase, Looker).
Strong analytical thinking: comfortable framing business questions, exploring data, and turning results into clear insights.
Ability to communicate complex topics in simple, concise language to non‑technical stakeholders.
Preferred Qualifications:
Experience with event‑driven data (logs, clickstream, customer journey, contact flows, tracking data).
Knowledge of data modeling techniques (e.g., dimensional modeling, Kimball, data vault, star/snowflake schema).
Experience with A/B testing, experimentation platforms, or causal analysis.
Familiarity with ML pipelines or working with data science teams (e.g., routing models, QA scoring, forecasting, recommendations).
Experience in online consumer products, travel, e‑commerce, or marketplaces.
Hands‑on frontend or backend development experience (e.g., building internal tools in React/Next.js, Node.js, or similar frameworks) used by operations or support teams.
Prior experience in a Staff or Tech‑Lead role, driving technical direction and mentoring multiple engineers/analysts.
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