Sobre esta vaga de Data Engineer na Audinate
Find Your Connection to Endless Possibilities
With Audinate, you’ll find your connection to the world of sound and vision.
We lead the world in AV networking media to pioneer the way people see and hear. Our products seamlessly manage hundreds of audio and video channels with one network, making us the industry standard. You’ll find us in the largest companies and institutions like the Sydney Opera House, NFL Media Headquarters, Microsoft, and even a 900-year old cathedral featured in Harry Potter.
Our core values are the foundation of everything we do: Excellence, Courage, Integrity, Innovation, and Teamwork.
If this resonates with you, this is the place for you.
What you’ll experience with us
About the role
As our Data Engineer, you’ll turn the data our business depends on into pipelines, models, and reports that people can trust every day. Your mission is to make our data products last: well-engineered, documented, tested, and easy to operate, so the insights we produce build on each other over time.
You’ll work alongside a senior data scientist and report to our Data & Insights Manager. Within the team, you’ll own the analytics engineering craft end to end: this means building the pipelines that move and shape our data, designing the models the business uses to make decisions, and creating the dashboards that Sales, Marketing, Finance, Product, and Strategy rely on to run their day-to-day operations.
What you'll be doing
- Building and maintaining the data pipelines that bring in, transform, and prepare Audinate’s commercial and operational data for analysis.
- Developing and looking after our modelled data layer (e.g. dbt), the foundation the business uses to understand itself, with version control, peer review, testing, and documentation built into how you work.
- Creating and maintaining the reporting tools our internal customers rely on to run their work, including dashboards, scheduled reports, and self-service exploration powered by LLM-based tools, for teams across Sales, Marketing, Finance, Product, and Strategy.
- Turning the team’s proven exploratory analyses into robust, supportable data products that keep delivering commercial value.
- Handling the platform and lightweight infrastructure work that keeps our stack running smoothly, including orchestration, deployment, monitoring, and data quality checks, in partnership with our internal DevOps team.
- Working within the platform roadmap while bringing your own recommendations on tooling and modelling approaches.
- Partnering closely with the team’s data scientist.
What we're looking for
- Deep experience in an analytics engineering, data engineering ideally within a B2B technology business.
- A track record of strong analytics engineering practice, where version control (git), peer review, testing, and documentation are built into your workflow rather than added at the end.
- Strong hands-on skills in Python, SQL, and NoSQL (e.g. MongoDB), with code that’s well structured and easy for others to pick up and run with.
- Practical experience building end-to-end data pipelines, covering ingestion, transformation, scheduling, and monitoring, using modern tools such as dbt, Airflow, Dagster, Prefect, or similar.
- Experience working with a modern cloud data warehouse (e.g. Snowflake, BigQuery, Databricks, Redshift), including setting it up so others across the organisation can use it easily and safely on a self-service basis.
- Experience with a modern BI or reporting tool (e.g. Metabase, Superset, Looker, Sigma, Lightdash), and the ability to build dashboards that non-technical users genuinely use.
- Comfort taking on light platform and infrastructure work, such as container basics, scheduled jobs, secrets management, and core cloud services (AWS, Azure, or GCP).
- Clear written communication skills, so you can document a pipeline or metric definition in a way the next person, whether an engineer or a business user, can understand without needing you.
- Sound judgement about when to seek direction and when to make the call yourself.
- A tertiary qualification in a relevant field (e.g. computer science, engineering, mathematics, statistics, information systems), or equivalent experience.
Nice to have
- Experience bringing AI or LLM tools into the data workflow, whether for data enrichment, documentation, or analytical support.
- Experience working with business systems such as CRMs (e.g. Salesforce, HubSpot) and accounting platforms (e.g. NetSuite).
- Familiarity with the professional AV industry, or with hardware-software businesses that sell through an OEM channel.
