Senior Data Engineer

iTeamly is offering a Senior Data Engineer role focused on technical ownership of data solutions involving large-scale social and web data, combining engineering, data discovery, and metric development.

• 5+ years of commercial experience in data engineering, analytics engineering, or a strongly technical data role.
• Strong hands-on experience with Python and SQL.
• Strong practical experience with dbt.
• Experience working with Snowflake.
• Experience building or developing reliable data pipelines and transformations.
• Strong understanding of data quality and automated testing.
• Ability to independently own complex technical problems from definition through implementation.
• Experience translating business or product requirements into technical solutions.
• High level of autonomy, technical judgement, and ownership.

Nice to have:

• Experience working with social media, web, trend, or behavioural data.
• Experience integrating external APIs and new data sources.
• Experience with Airflow or other orchestration tools.
• Experience with Data Vault modelling.
• Experience with Tableau and/or Power BI.
• Broader experience with data modelling and data warehouse concepts.
• Experience designing analytical metrics and data products.
• Experience building prototypes to validate new data approaches.
• Experience introducing automated data quality checks and testing practices.
• Experience assessing unfamiliar datasets for quality and business value.
• Previous exposure to trend or signal analysis.
• Practical experience with AI coding tools such as Copilot, Cursor, Codex, or Claude Code.

• Owning the technical approach for extracting, transforming, and scoring signals from large social and web datasets.
• Designing and developing reliable data transformations and pipelines.
• Evaluating new data sources for incorporation into existing workflows.
• Defining and validating metrics identifying meaningful trends and signals.
• Taking ownership of complex technical implementations.
• Translating ambiguous business and product requirements into clear technical solutions.
• Cleaning and preparing large datasets ensuring quality and consistency.
• Building automated testing into data transformations and pipelines.
• Developing rapid prototypes to validate technical approaches.
• Delivering clean, reusable code, pipelines, and datasets.
• Supporting technical decisions and shaping approaches to new data challenges.
• Using AI-assisted development tools while maintaining ownership of engineering quality.