[Hiring] Data Engineer @In All Media Inc

Role Description

You will join a core Data Engineering organization, working in close partnership with the Data Platform team to drive a major data platform modernization initiative. The primary focus of this project is executing pre-designed migration blueprints for approximately 1,000 datasets to Snowflake with Airflow 3 orchestration. You will play a crucial role in accelerating time-sensitive infrastructure goals by evolving legacy Lakehouse formats to Apache Iceberg, modernizing streaming infrastructure across cloud regions, and adapting existing pipelines to uphold strict data quality and performance standards.

Key Responsibilities

• Pipeline Migration & Upgrades: Execute the end-to-end migration of approximately 1,000 datasets to Snowflake, including upgrading orchestration workflows from Apache Airflow 2 to Airflow 3.
• Storage & Data Lake Modernization: Migrate legacy Hive data structures and datasets to Apache Iceberg format using the Snowpark Connect framework/connectors.
• Streaming Infrastructure & Region Migration: Reconfigure, update, and migrate Confluent/Apache Kafka streaming connections across cloud regions (e.g., migrating from US East 1 to US East 2).
• Pipeline Adaptation & Performance Tuning: Adapt existing data pipelines to target Snowflake environments while optimizing SQL queries (utilizing CTEs, window functions, and complex joins) to ensure high performance and data reliability.
• Workflow Scheduling & Monitoring: Manage, test, and debug DAGs (Directed Acyclic Graphs) and workflow scheduling within Apache Airflow.
• Testing & Quality Assurance: Implement testing strategies and conduct hands-on code debugging to guarantee data quality and consistency throughout the platform modernization.

Qualifications

• Minimum 2+ years of hands-on data engineering experience working with large-scale datasets, Apache Airflow, and Apache Spark.
• Strong software engineering fundamentals with hands-on Python development experience and advanced SQL skills (complex joins, CTEs, window functions, and query tuning).
• Proven experience with Snowflake data modeling and schema architecture.
• Direct experience building and testing pipelines using Apache Airflow, Apache Kafka, and Apache Spark / PySpark.
• Expertise working with Apache Iceberg and Apache Parquet data formats, alongside AWS S3 (data lakes, bucket partitioning, and lifecycle policies).
• Ability to define testing strategies and independently debug complex distributed data pipelines.
• Strong communication skills with a proven track record of working independently in a fast-paced environment.

Requirements

• This role requires the use of a client-provisioned AWS WorkSpace. Candidates must demonstrate good connectivity to an AWS WorkSpace region by completing the connectivity test at https://clients.amazonworkspaces.com/Health . Please note: You will be required to submit a screenshot of these results as supporting evidence with your application.

Benefits

• Fully remote work environment.
• Strong preference for candidates located in the EMEA (Europe, Middle East, Africa) or APAC (Asia-Pacific) regions.
• Time zone coverage required until at least 12:00 PM PT (Pacific Time) to ensure seamless overlap with the core engineering team.

Nice-to-Have Skills

• Experience with enterprise Snowflake migrations (warehouse modernization, cost and performance tuning).
• Direct experience with Apache Airflow platform migrations (version upgrades, executor changes, or platform moves).
• Hands-on experience using AI-assisted coding/development tools to accelerate workflow velocity.

Language

• All interviews, documentation, and daily communication will be in English.