Senior Data Engineer (Pipelines & Data Quality) - Data Platform
On-site Nawamin Road Full-time Senior Data Engineering
At a glance
Senior Data Engineering role at Makro PRO. Nawamin Road · full-time.
Pay not stated Data Engineer salary
RoleData Engineering
SenioritySenior
LocationNawamin Road
WorkplaceOn-site
EmploymentFull-time
PostedJul 2, 2026 · 3mo ago
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build a deployment gate that checks pipeline names, metadata, configuration, quality rules and cluster-policy references
- Roll out data-quality checks on production pipelines, create a domain onboarding template and route alerts by severity
- Publish a near-real-time dashboard showing freshness, completeness, accuracy and quality health across domains, sources and tables
- Agree on source-system change processes and service levels, automate schema alerts, and trace data lineage end to end
- Set the legacy-pipeline migration playbook, mentor engineers, and lead major quality-incident reviews and prevention work
What you bring
- A bachelor’s degree in computer science, data engineering or a related discipline
- At least five years building and operating production pipelines on a major lakehouse or warehouse platform
- Strong PySpark and SQL skills, including experience tuning Spark for production workloads
- Hands-on depth with data-quality frameworks, including setting service levels, thresholds and alert behavior
- Experience with layered lakehouse architecture and Git-based delivery practices for data code
Who this fits
This senior role suits an engineer who sets pipeline standards and quality controls while mentoring others, rather than handling every migration personally. It is based on-site on Nawamin Road in Thailand.
From the employer
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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