CP Axtra

Wholesale and retail distributor with omnichannel

Senior Data Engineer (Pipelines & Data Quality) - Pipelines & Data Quality, Data Platform

Data Management +1Confirmed live in the last 24 hours

Full-timeThailandNo salary listedIn-person

About the Job

Requirements

A bachelor's degree in Computer Science, Data Engineering, or a related discipline is required.
At least 5 years of experience designing, building, and operating production data pipelines on a major lakehouse or warehouse such as Databricks, Snowflake, or BigQuery is required.
Strong PySpark and SQL skills and understanding of Spark performance tuning at production scale are required.
Deep experience with data-quality frameworks such as Great Expectations, dbt tests, Soda, or Monte Carlo is required, including defining service-level agreements, setting thresholds, and tuning alert noise.
Experience building and operating medallion or multi-layer lakehouse architectures with explicit transformation layers is required.
Solid Git and continuous integration experience for data code, with knowledge of testing data transformations, is required.
Ability to define and enforce standards for naming, partitioning, retention, and personally identifiable information tagging, and review pull requests against them, is required.
Experience with a cloud platform is required; Azure is preferred, and AWS or Google Cloud Platform experience is transferable.

Responsibilities

Design, ship, and operate a pipeline-compliance checker that validates naming, metadata, configuration schema, data-quality rule declarations, and cluster-policy references on each new deployment.
Deploy a data-quality framework across new production pipelines, build a domain-onboarding template, and configure alert routing by severity.
Build and publish the Data Quality Dashboard showing quality health by domain, source, and table, with near-real-time refresh and measures of freshness, completeness, and accuracy.
Establish Source Change Management agreements with key source systems, including service-level agreement contracts, change-request processes, and automated schema-change alerting, and map source lineage end to end.
Lead the migration playbook to standardize the legacy pipeline estate and mentor engineers executing migrations; own the playbook rather than every migration.
Drive data-incident reduction through prevention mechanisms, including the compliance gate, data-quality framework, Data Change Management, and lineage; lead incident response and post-mortems for major data-quality failures.
Partner with platform engineering on Event Stream domain-event schemas and data-product contracts.
Author runbooks, conduct code reviews at senior level, and contribute to engineering culture.

Desired Qualifications

Streaming experience with Spark Structured Streaming, Delta Live Tables, Flink, or Kafka Streams.
Data-modeling discipline using Kimball or Data Vault 2.0, with clear rationale, and production experience with Unity Catalog, including lineage, tags, and row-level security.
Retail data exposure, including point of sale, inventory, replenishment, or loyalty, and business intelligence optimization for Power BI consumption.
Vendor certifications such as Databricks Data Engineer Professional or Azure Data Engineer Associate.

Senior Data Engineer (Pipelines & Data Quality) - Data Platform

CP Axtra · Nawamin Road, Thailand · Full-time · Posted 2026-07-02

Workplace: on_site

Department: Technology

Description

We are seeking an experienced Senior Data Engineer to own the pipeline-standardization and data-quality program for the enterprise lakehouse. This role ships compliance gates that block non-compliant deployments, stands up the data-quality framework, builds the dashboards business users trust, and drives measurable reductions in data incidents across the retail data estate.

Key Responsibilities:

Design, ship, and operate a pipeline-compliance checker that validates naming, metadata, config schema, DQ-rule declarations, and cluster-policy reference on every new deployment.
Deploy a data-quality framework (Great Expectations, Databricks DQ Rules, or equivalent) across new production pipelines; build a domain onboarding template; configure alert routing by severity.
Build and publish the Data Quality Dashboard — quality health by domain, source, table; near-real-time refresh; freshness, completeness, accuracy.
Establish Source Change Management agreements with key source systems (SLA contracts, change-request process, automated schema-change alerting); map source lineage end-to-end.
Lead the migration playbook to bring the legacy pipeline estate to standard; mentor engineers executing migration; own the playbook, not every migration.
Drive data-incident reduction through prevention (compliance gate, DQ framework, DCM, lineage), not reactive firefighting; lead incident response and post-mortems for major DQ failures.
Partner with platform engineering on Event Stream domain-event schemas and data-product contracts.
Author runbooks, code review at senior level, and contribute to engineering culture.

Requirements

Bachelor's degree in Computer Science, Data Engineering, or a related discipline.
5+ years designing, building, and operating production data pipelines on a major lakehouse or warehouse (Databricks, Snowflake, BigQuery).
Strong PySpark and SQL; understands Spark performance tuning at production scale.
Deep experience with data-quality frameworks (Great Expectations, dbt tests, Soda, Monte Carlo) — has defined SLAs, set thresholds, tuned alert noise.
Built and operated medallion / multi-layer lakehouse architectures with explicit transformation layers.
Solid Git / CI experience for data code; opinions on testing data transformations.
Comfortable defining and enforcing standards (naming, partitioning, retention, PII tagging) and reviewing PRs against them.
Cloud platform experience (Azure preferred; AWS / GCP transferable).

Preferred Qualifications

Streaming experience (Spark Structured Streaming, Delta Live Tables, Flink, Kafka Streams).
Data modeling discipline (Kimball, Data Vault 2.0) with clear rationale; Unity Catalog production experience (lineage, tags, RLS).
Retail data exposure — POS, inventory, replenishment, loyalty — and BI optimization for Power BI consumption.
Vendor certifications such as Databricks Data Engineer Professional or Azure Data Engineer Associate.

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About the Company

CP Axtra

Wholesale and retail distributor with omnichannel

CP Axtra runs wholesale and retail networks in Thailand and across Asia, with two main segments: Makro for B2B wholesale and Lotus's for B2C retail in hypermarkets and supermarkets. It sources goods from suppliers and sells them through its stores and online channels, while also earning from leasing mall space; digital tools like the Makro PRO app and partnerships with Cainiao and Microsoft help manage inventory and personalize shopping. The company combines a large B2B wholesale ecosystem with a broad B2C retail footprint and a unified omnichannel experience to connect physical and online shopping. Its goal is to become a leading RetailTech company in Asia by improving supply chains, expanding its omnichannel reach, and growing its ecosystem of stores, online services, and mall spaces.

Company size: 1,001-5,000
Company stage: IPO
Headquarters: Bangkok, Thailand
Founded: 1988

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