Lead Data Engineer

Manila Recruitment

. Own the architecture for ingestion, transformation, and analytics delivery across products .

Posted 9/19/2026full-timeAlabang • PhilippinesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in data engineering, focusing on building scalable data pipelines and implementing data architecture principles, including Medallion Architecture. Proficient in data quality, governance, and performance management while leading teams and guiding technical direction.

Highest-signal resume keywords

Data Engineering ExperienceMedallion Architecture ImplementationMicrosoft Fabric ProficiencyProduction-Scale Data PipelinesPower BI Data Modelling

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ArchitectureData ModellingAdvanced SQLPythonPySparkData Quality ChecksIncremental LoadsCI/CD for DataDimensional ModellingData Transformation

Soft Skills

Technical JudgementProblem-SolvingTeam LeadershipCoachingCollaboration

Tools & Technologies

Microsoft FabricDatabricksSnowflakeAzure DevOpsGitHub ActionsPower BIFabric Git Integration

Industry Keywords

Data GovernanceData ObservabilityData LineageAccess ControlRow-Level SecurityPrivacy

Tech Stack

Tools & technologies

AzurePySparkPythonSQL

About the role

Key responsibilities & impact

Own the architecture for ingestion, transformation, and analytics delivery across products
Define scalable and reusable patterns for pipelines, storage layouts, workspace structures, data modelling, and analytics
Build production-scale pipelines across the Medallion Architecture from Bronze to Silver to Gold
Establish data engineering standards, schema conventions, transformation patterns, naming, monitoring, and reporting-readiness criteria
Define how coding agents are used, including specification, validation, and controls
Develop first working versions and production code with idempotency, safe replay, incremental logic, and clean layer separation
Deploy pipelines through source control and CI/CD, with versioning, monitoring, drift detection, alerting, and tested recovery paths
Own platform performance, reliability, recovery, and cost as data volume grows
Implement data quality checks and validation at each layer boundary
Maintain consistent metric definitions and collaborate on access control, row-level security, and privacy
Architect semantic-layer models, data marts, and shared datasets for Power BI
Guide analysts and report developers on modelling, performance, and reuse
Lead a small team of data engineers by guiding solution design, reviewing work, resolving complex issues, and coaching on lineage, quality, and observability
Establish practices for a reliable, reusable, and scalable data platform

Requirements

What you’ll need

8+ years of hands-on data engineering experience, with strong data engineering fundamentals and experience designing and building data solutions end-to-end
At least 2 years of experience in a Lead Data Engineer, Technical Lead, or people leadership/management role, with the ability to provide technical direction while remaining hands-on
Strong understanding of data architecture and data modelling, including dimensional modelling and layered designs such as Medallion Architecture (Bronze → Silver → Gold), with proven experience implementing these in a project
Hands-on experience with Microsoft Fabric is required; strong Databricks, Snowflake, or Synapse backgrounds are credible if the candidate can work with Fabric
Proven experience building production-scale data pipelines and transformations from scratch, including ingestion from operational source systems, advanced SQL, incremental loads, and Python or PySpark
End-to-end data engineering experience from source ingestion and pipeline development through data modelling and the Power BI reporting layer
Working knowledge of Power BI, including dataflows, dataset design, semantic modelling, and DAX
Experience with production ownership of data platforms, including monitoring, refresh reliability, recovery, performance, and cost
Knowledge of data governance, quality, observability, lineage, validation, access control, row-level security, privacy, and CI/CD for data using Azure DevOps, GitHub Actions, or Fabric Git integration
Strong technical judgement and problem-solving skills, with the ability to independently determine the appropriate technical approach, explain the reasoning, and take ownership through resolution
Knowledge and awareness of agentic engineering; hands-on experience is advantageous