Senior Data Engineer

Canela Media is looking to add a passionate and inspiring Senior Data Engineer to our Data Engineering team. This role will sit with the CTO’s organization, and is a key leader that supports our global, teams, clients, and the millions of people who use our media/ entertainment apps, across the world. We’re looking for a leader who is excited by building world-class data engineering solutions centred around excellence, iteration, and innovation.
You will partner with other members of the CTO’s leadership team as well as the leaders of Marketing, Product, Sales, Finance, Legal, and Operations. You believe in and can help nurture our values of curiosity, inclusiveness, and collaboration.
Responsibilities:Spend time with stakeholders and meet with them to shape a collective consensus on technical solutions. Write technical documentation and requirements. Load and query cloud-hosted databases such as Redshift and Snowflake Designing and maintaining scalable data pipelines (batch and near-real-time) Managing data ingestion from multiple sources (APIs, S3, databases, third-party providers) Building and optimizing our data lake architecture on AWS (S3, Athena, Glue) Orchestrating workflows using Airflow (MWAA) Ensuring data quality, performance, and cost efficiency across our pipelines Production ownership and Troubleshooting
Overall scope:- Assist in designing and evolving data lake architecture (multi-layered, partitioned, scalable) - Building and Supporting pipelines end-to-end in production environments - Handling performance, reliability, and operational challenges at scale
Manage & Support:- Support Multi-account AWS environments and cross-account data access - Support Large-scale orchestration (complex Airflow DAGs) - Perform integration with multiple external data sources (APIs, third-party providers, distributed S3 inputs)
Performance and Cost Optimization:- Athena queries and partitioning strategies - S3 storage layouts - Glue job performance and resource usage
Production / Infrastructure Management - Debugging pipelines in production - Handling failures, retries, and data inconsistencies - Ensuring data quality and reliability
Qualifications: Bachelor or above in computer science or a related quantitative field; or equivalent practical experience demonstrating advanced technical expertise At least 2+ years' experience as an engineer (or more senior) within a Data Engineering function. Strong knowledge and expertise implementing modern DevOps practices and tooling (e.g., Git-based workflows, CI/CD, containers, and public cloud platforms, Infrastructure-as-Code). Experience using Infrastructure-as-Code (IaC) i.e. Terraform or similar, is preferred. Expert skills in Python and or Java, and SQL. Strong skills and experience building ETL processes that handle batch and real-time data ingest and can build data stores / databases that can be used for querying and analysis. Experience with Datalake architecture and partitioning strategies (S3 + Athena) Strong experience building data Pipeline orchestrations using Airflow/MWAA that are reliable and scalable. Experience with Data modeling and dataset curation for analytics. Advanced knowledge of AWS-native data services (Glue, Lambda, Step Functions). Advanced knowledge of SQL and Python-based data processing (including tools like awswrangler or PySpark when needed). Experience with Agile Scrum Excellent written and verbal communication skills Media Industry experience (preferred)