Senior Data Engineer

What we’re looking for

We are looking for a seasoned data engineer to join a team of highly skilled data engineers and data architects to build and manage end-to-end data pipelines (batch and near real-time) using modern cloud technologies. This is a role in the central data organization at SurveyMonkey that provides actionable insights into all essential business functions of the organization

As a Senior Data Engineer, you will lead data engineering initiatives and build end-to-end Analytical solutions that are highly available, scalable, stable, secure, and cost-effective. You will be reporting to the Director of Data Engineering

• Design, architect and build data pipelines to support existing data models 
• Data quality: Build quality checks in the end-to-end data pipelines
• Build new Data models (Fact vs Dimension). Write performant/idempotent transformations in Snowflake
• Build data pipeline using Python scripting (in a modular/loop context) Write well-tested, production-ready code in Python and Snowflake SQL
• Hands-on experience implementing ETL (or ELT) best practices 
• Translate business requirements, to technical specifications,  form project scope, and deliver deployable code.
• Write complex data engineering Snowflake - SQL jobs that perform sophisticated queries on the entirety of our datasets
• Document our systems for internal and external stakeholders
• Monitor and debug data pipelines running on Airflow
• Participate in code reviews
• Mentor data engineers on best practices and coding standards

We'd love to hear from people with

• 7+ years experience in data engineering  and Data warehousing technologies
• 3+ years experience in Snowflake/ETL or similar technologies like Redshift
• Understanding of digital marketing and systems concepts, processes, and data
• Experience working extensively with Digital Marketing: Google Ads, Meta Ads, Paid Media Marketing, etc
• Experience building Data integrations with Marketing systems
• Experience with AWS cloud services: S3, EC2, RDS, Spark, EMR etc
• Experience with object-oriented/object function scripting languages: Python (preferred), Java, Scala, etc.
• Experience in orchestrating, automating, and deploying production data pipelines using Airflow/Luigi, etc
• Experience with DevOPS: Git, Github actions, CI/CD pipelines, Terraform, etc
• Experience with tools such as DBT or other similar technologies
• Experience with transforming, and developing data structures, metadata, dependency, and data workflows to support an Analytics function
• In-depth knowledge of Data lakes, EDW concepts, and data modeling (Star, Snowflake, and Galaxy schemas)

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