Data Developer

Aylo Careers; Montreal; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

Aylo Careers in Montreal is seeking an Analytics Engineer to design scalable data models and pipelines supporting infrastructure, security, and operations. You will work with SQL, dbt, and modern data tooling to deliver trusted datasets and dashboards.

Join a hybrid team with opportunities to influence data governance, cost optimization, and performance. This role involves cross-functional collaboration with engineering and security to define metrics and improve data accessibility.

Qualifications

• 3+ years in analytics engineering, data analytics, or a similar technical role.
• Bachelor's degree in Statistics, Computer Science, Engineering, Finance, or related analytical field.
• Strong SQL, plus experience building data models (dimensional modeling, warehousing, or data lake environments).
• Hands-on experience with a transformation framework such as dbt, and with version control and reproducible data workflows.
• Experience with BI tools (Power BI, Tableau) and semantic layer design.
• Working knowledge of data quality, governance, and privacy practices.
• Experience with technical data domains such as infrastructure, systems, security, or engineering telemetry.
• Ability to translate complex technical data into insights stakeholders can act on.

Responsibilities

• Build and maintain scalable data models and transformation pipelines for infrastructure, security, and operational analytics using SQL and modern data tooling
• Treat the platform as code: version-controlled transformations, tested pipelines, peer-reviewed changes, and reproducible environments
• Own the security data model, including entity resolution across asset and identity sources, a common cross-domain schema, and partitioning, retention, and cost strategy
• Deliver curated datasets, semantic layers, and dashboards that make reporting on system performance, outages, security health, and cost consistent and trusted
• Enforce data quality and governance through validation checks, freshness SLAs, reconciliation, lineage tracking, access controls, and sensitive-data handling
• Partner with engineering, infrastructure, and security teams to define metrics, standardize definitions, and improve data accessibility
• Analyze vendor performance and cost data to support infrastructure investment and optimization decisions
• Improve the analytics platform itself: workflow orchestration, query performance, and architecture

Skills

SQL

Data modeling

BI concepts

Semantic layer design

Python

Data quality

Cost optimization

Analytics engineering

Education

Bachelor's degree in Statistics, Computer Science, Engineering, Finance, or related analytical field

Tools

dbt

Power BI

Tableau

Airflow

Delta Lake

Iceberg

Job description

Established in 2004, we are a tech pioneer offering world-class adult entertainment and games on some of the internet’s safest and most popular platforms. With the support of an international team of dynamic and collaborative innovators, we are on a mission to enable safe user experiences and empower our communities by celebrating diversity, inclusion, and expression — all while maintaining robust trust-and-safety protocols.

We embrace the best of both worlds! Local talent can thrive in our collaborative office space with the flexibility of a hybrid work environment, while remote team members play an integral role in shaping our dynamic culture from afar. We have offices in Montreal (Quebec), Austin (Texas) and Nicosia (Cyprus).

*A select number of positions require full-time in office attendance*

Make your mark in a role where strong analytics engineering directly supports critical operations at scale.You’lltackle complex data challengesandhelp shape modern data practice.Joina focused, supportive team that cares about both outcomes and sustainability by helping to define how performance, resilience, security, and cost are measured across a large organization

Whatyou'llbe doing:

• Build andmaintainscalable data models and transformation pipelines for infrastructure, security, and operational analytics using SQL and modern data tooling
• Treat the platform as code: version-controlled transformations, tested pipelines, peer-reviewed changes, and reproducible environments
• Own the security data model, including entity resolution across asset and identity sources, a common cross-domain schema, and partitioning, retention, and cost strategy
• Deliver curated datasets, semantic layers, and dashboards that make reporting on system performance, outages, security health, and cost consistent and trusted
• Enforce data quality and governance through validation checks, freshness SLAs, reconciliation, lineage tracking, access controls, and sensitive-data handling
• Partner with engineering, infrastructure, and security teams to define metrics, standardize definitions, and improve data accessibility
• Analyze vendor performance and cost data to support infrastructure investment and optimization decisions
• Improve the analytics platform itself: workflow orchestration, query performance, and architecture

Must haves:

• 3+ years in analytics engineering, data analytics, or a similar technical role
• Bachelor's degree in Statistics, Computer Science, Engineering, Finance, or a related analytical field
• Strong SQL, plus experience building data models (dimensional modeling, warehousing, or data lake environments)
• Hands-on experience with a transformation framework such asdbt, and with version control and reproducible data workflows
• Experience with BI tools (Power BI, Tableau) and semantic layer design
• Working knowledge of data quality, governance, and privacy practices
• Experience with technical data domains such as infrastructure, systems, security, or engineering telemetry
• Ability to translate complex technical data intoinsightsstakeholders can act on

Nice to haves:

• Python for data transformation, pipeline development, or analysis
• Workflow orchestration tools (e.g., Airflow) and data lake table formats (Delta, Iceberg)
• Understanding of cloud systems and observability metrics
• Experience with vendor performance or financial analysis
• AI/ML governance frameworks and associated data controls
• End-to-endMLOps: feature engineering on large-scale telemetry (pandas/Polars,PySpark), anomalydetectionand classification models (scikit-learn,XGBoost,PyOD), and experiment tracking with a versioned model registry (MLflow)

As an equal opportunity employer, we celebrate diversity and are committed to creating an inclusive environment for all employees

In this role you may be exposed to adult content