Data Engineer

Audinate

. Build and maintain data pipelines for Audinate’s commercial and operational data .

Posted 11/3/2026full-timeManila • PhilippinesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining data pipelines, utilizing strong analytics engineering practices, and developing robust data products. Proficient in Python, SQL, and modern cloud data warehouses, with a focus on effective communication and collaboration within cross-functional teams.

Highest-signal resume keywords

Analytics EngineeringData Pipeline DevelopmentPython ProgrammingSQL ProficiencyCloud Data Warehousing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringVersion ControlData TransformationData Quality ChecksEnd-to-End Data PipelinesTestingDocumentationNoSQLData ModelingExploratory Analysis

Soft Skills

Clear Written CommunicationSound Judgement

Tools & Technologies

GitDbtAirflowSnowflakeBigQueryDatabricksLookerAWSAzureGCP

Certifications & Qualifications

Tertiary Qualification in Relevant Field

Industry Keywords

B2B TechnologyBusiness IntelligenceData WarehousingData ProductsProfessional AV Industry

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSAzureBigQueryCloudGoogle Cloud PlatformMongoDBNoSQLPythonSnowflakeSQL

About the role

Key responsibilities & impact

Build and maintain data pipelines for Audinate’s commercial and operational data
Develop and maintain the modelled data layer using practices such as version control, peer review, testing, and documentation
Create and maintain dashboards, scheduled reports, and self-service exploration tools for Sales, Marketing, Finance, Product, and Strategy
Turn exploratory analyses into robust, supportable data products
Handle orchestration, deployment, monitoring, and data quality checks in partnership with DevOps
Contribute recommendations on tooling and modelling approaches within the platform roadmap
Partner closely with the team’s data scientist
Report to the Data & Insights Manager

Requirements

What you’ll need

Deep experience in analytics engineering or data engineering, ideally within a B2B technology business
Strong analytics engineering practices with version control (Git), peer review, testing, and documentation
Strong hands-on skills in Python, SQL, and NoSQL (e.g. MongoDB)
Practical experience building end-to-end data pipelines covering ingestion, transformation, scheduling, and monitoring
Experience with dbt, Airflow, Dagster, Prefect, or similar tools
Experience with modern cloud data warehouses such as Snowflake, BigQuery, Databricks, or Redshift
Experience with modern BI/reporting tools such as Metabase, Superset, Looker, Sigma, or Lightdash
Comfort with container basics, scheduled jobs, secrets management, and AWS, Azure, or GCP
Clear written communication skills for documenting pipelines and metric definitions
Sound judgement about when to seek direction and when to make decisions independently
Tertiary qualification in a relevant field or equivalent experience
Nice to have: experience with AI or LLM tools, CRMs, accounting platforms, or the professional AV industry

Benefits

Comp & perks

Competitive salary
Annual bonus
Equity incentive plan
Flexible working (2 days per week in office)
Home office support
Volunteering leave
Diverse, values-based culture