What is your mission? 

You will provide the best service to our partner brands by performing these tasks:

• Design, build, and optimize ETL/ELT pipelines for reliability, performance, and cost-efficiency
• Develop and maintain dbt models and documentation
• Create and manage Airflow / Cloud Composer DAGs with robust scheduling and monitoring
• Architect and implement scalable Snowflake solutions
• Develop dimensional models and data marts
• Implement FDW/federated connections for cross-database queries
• Integrate data from APIs, databases, and files across multiple source systems
• Deploy and manage data infrastructure on GCP
• Implement and monitor data quality checks, SLAs, lineage, and observability
• Maintain documentation, data dictionaries, and process guides
• Conduct code reviews and collaborate cross-functionally

Who are we looking for?

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (or equivalent practical experience)
• 1–2+ years professional experience in data engineering or analytics engineering
• Advanced SQL including complex queries, optimization, indexing, CTEs, and window functions
• Experience with PostgreSQL, MySQL, SQL Server
• Hands-on experience with dbt for transformation and modeling
• Strong experience with Snowflake including streams, tasks, optimization, and time travel
• Familiarity with GCP services such as BigQuery, Cloud Storage, Cloud Functions, Cloud Composer, Dataflow, and Pub/Sub
• Experience with Apache Airflow DAG development, workflow design, error handling, and monitoring
• ETL/ELT design and implementation including incremental loads and CDC
• Ability to handle structured and semi-structured data formats such as JSON, XML, and Parquet
• Proficiency in Python (Pandas, NumPy, SQLAlchemy) and bash/shell scripting
• Experience integrating REST APIs and using Git for version control
• Familiarity with CI/CD for data pipelines
• Knowledge of FDW/federated queries such as postgres_fdw
• Understanding of data lineage, impact analysis, and data quality frameworks
• Exposure to real-time streaming tools like Kafka or Pub/Sub
• Experience with modern ingestion tools such as Fivetran, Stitch, or Airbyte
• Knowledge of reverse ETL tools like Census or Hightouch
• Familiarity with infrastructure-as-code tools such as Terraform and distributed computing frameworks like Spark or Beam
• Awareness of data governance and privacy standards such as GDPR and CCPA
• Certifications (nice-to-have): Snowflake SnowPro, Google Cloud Professional Data Engineer, dbt Analytics Engineering
• Strong communication, documentation discipline, collaboration, problem-solving, and attention to detail