Senior Data Engineer (Pipelines & Data Quality) - Data Platform
LocationNawamin Road, Bangkok, Thailand
Typefull-time
DepartmentTechnology
Company size10,000+ people
First seen1w ago
Last seen1d ago
About the role
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:
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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.
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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.
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Build and publish the Data Quality Dashboard — quality health by domain, source, table; near-real-time refresh; freshness, completeness, accuracy.
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Establish Source Change Management agreements with key source systems (SLA contracts, change-request process, automated schema-change alerting); map source lineage end-to-end.
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Lead the migration playbook to bring the legacy pipeline estate to standard; mentor engineers executing migration; own the playbook, not every migration.
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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.
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Partner with platform engineering on Event Stream domain-event schemas and data-product contracts.
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Author runbooks, code review at senior level, and contribute to engineering culture.
Requirements
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Bachelor’s degree in Computer Science, Data Engineering, or a related discipline.
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5+ years designing, building, and operating production data pipelines on a major lakehouse or warehouse (Databricks, Snowflake, BigQuery).
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Strong PySpark and SQL; understands Spark performance tuning at production scale.
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Deep experience with data-quality frameworks (Great Expectations, dbt tests, Soda, Monte Carlo) — has defined SLAs, set thresholds, tuned alert noise.
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Built and operated medallion / multi-layer lakehouse architectures with explicit transformation layers.
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Solid Git / CI experience for data code; opinions on testing data transformations.
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Comfortable defining and enforcing standards (naming, partitioning, retention, PII tagging) and reviewing PRs against them.