Data Engineer

About Traffic Label

Traffic Label is a fast-growing, data-driven technology company operating in the iGaming and affiliate marketing space, building scalable products focused on performance, analytics, and automation across global markets.

What We're Building

Our Data Platform team builds and operates a Customer Data Platform (CDP) that powers marketing activation, identity resolution, and revenue attribution across multiple brands. The platform ingests player data from casino systems, enriches it
through an MDM layer, and activates it through email service providers (DotDigital, Adestra, FastTrack) for personalised campaigns.

The marketing activation flow — from player registration through identity matching to campaign delivery and attribution — is the core value the platform delivers. You will work across this entire flow: ingestion, transformation, reverse ETL
to ESPs, and campaign feedback loops.

Stack: Python, Snowflake, dbt Core, Astronomer (Airflow), AWS Lambda, Terraform, GitHub Actions.

Key Responsibilities

• Build and maintain ingestion pipelines (REST APIs, SFTP, database replication) into Snowflake
• Write dbt models across bronze/silver/gold layers following established conventions
• Integrate with third-party ESPs (DotDigital, Adestra, FastTrack): push segments, pull back campaign events (opens, clicks, bounces, conversions)
• Develop and maintain AWS Lambda functions for data extraction and reverse ETL
• Build data models for identity resolution, segmentation, and attribution
• Configure and manage Airflow DAGs for orchestration
• Implement data quality checks, monitoring, and alerting
• Manage Snowflake and AWS infrastructure via Terraform
• Investigate and resolve data incidents (pipeline failures, data quality issues, PII exposure)

Core Skills

• 4+ years of experience developing and deploying data pipelines using Python in production (tested, CI/CD, error handling)
• 4+ years of experience writing complex SQL queries, performance tuning, Snowflake-optimised
• Strong experience with Snowflake: SnowPipe, external stages, RBAC, masking policies, warehouses
• Strong experience with dbt Core for data transformation, testing, and incremental models
• Experience with data warehouse design: star schemas, snowflake schemas, dimensions, facts, slowly changing dimensions
• Experience with data modelling methodologies: conceptual, logical, and physical models
• Experience developing and deploying ETL/ELT pipelines using Airflow or similar orchestration tools
• AI-native development workflow: daily use of AI coding tools (Copilot, Cursor, Claude Code, Kiro) for code generation, review, and debugging

Supporting Skills

• Spec-driven development: requirements spec AI generates implementation human reviews and iterates
• MCP (Model Context Protocol) for connecting AI agents to infrastructure and data tools
• Data quality frameworks: dbt tests, Soda, Monte Carlo, or equivalent
• AWS services (S3, Lambda, IAM, EventBridge)
• Terraform
• Git, CI/CD pipelines, Docker
• Data lineage and impact analysis

Nice to Have

• Kafka or streaming experience
• ESP integrations (DotDigital, Adestra, or similar marketing platforms)
• iGaming domain knowledge

What We Offer

• Opportunity to work on scalable, high-impact products in a growing iGaming business
• Collaborative and fast-paced engineering environment
• Exposure to modern technologies and architecture
• Competitive salary and performance-based incentives
• Flexible working environment and supportive team culture.