2128 Sr Data Engineer - Platform Modernization & Migration

🧭 About Inallmedia.com Inallmedia.com is a global technology and design firm focused on building impactful digital solutions through remote, distributed teams. We partner with international clients across industries, providing long-term technical expertise, product innovation, and team augmentation.

🚀 Project Overview 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.

💡 Must-Have Skills

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

✨ Nice-to-Have Skills

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

🌐 Time Zone & Collaboration The role is fully remote, with a strong preference for candidates located in the EMEA (Europe, Middle East, Africa) or APAC (Asia-Pacific) regions. Time zone coverage is required until at least 12:00 PM PT (Pacific Time) to ensure seamless overlap with the core engineering team.

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

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