Marketing Data Engineer (Remote)

#WeAreCrowdStrike and our mission is to stop breaches. As a global leader in cybersecurity, our team changed the game. Since our inception, our market leading cloud-native platform has offered unparalleled protection against the most sophisticated cyberattacks. We’re looking for people with limitless passion, a relentless focus on innovation and a fanatical commitment to the customer to join us in shaping the future of cybersecurity. Consistently recognized as a top workplace, CrowdStrike is committed to cultivating an inclusive, remote-first culture that offers people the autonomy and flexibility to balance the needs of work and life while taking their career to the next level. Interested in working for a company that sets the standard and leads with integrity? Join us on a mission that matters - one team, one fight.

About the Role:

The CrowdStrike Marketing Analytics team is seeking a talented and highly motivated Data Engineer who will power business strategy & execution by building clever system integrations, maintaining reliable data pipelines, and 

architecting scalable data solutions. This role requires close collaboration with Go-To-Market teams, IT, and Finance to establish optimized, source-of-truth datasets for cross-functional reporting. The ideal candidate considers themselves an engineer with a passion for creating technical solutions to solve business problems.

What You'll Do:

• Architect and develop scalable ETL/ELT pipelines to extract, transform, and load structured and unstructured data from multiple sources, ensuring efficient and reliable data flow.

• Build, optimize, and manage cloud-based data warehouses to handle large-scale data processing, storage, and retrieval with an emphasis on performance optimization.

• Develop and implement data integration solutions (both batch and near real time) with external systems and ensure seamless data exchange across platforms.

• Implement automated processes for data validation, ensuring high standards of data quality, accuracy, and integrity across all pipelines.

• Work closely with various teams (e.g., Data scientists, Marketing operations team, and business analysts) to understand data requirements, support analytics, and enable machine learning initiatives through data preparation.

• Collaborate with stakeholders to integrate data from various business systems, such as CRM and marketing automation platforms, ensuring that data is aligned with business processes and decision-making needs.

• Continuously monitor and fine-tune data workflows, SQL queries, and cloud resources to improve data processing performance, reduce latency, and optimize costs.

What You’ll Need:

• Bachelor’s degree in a STEM field.

• 5+ years of hands-on experience in data engineering.

• Expertise in SQL: Complex queries, optimization techniques, stored procedures, and automation of data workflows.

• Strong Python skills for data manipulation, automation (e.g., Pandas, PySpark), and scripting ETL workflows.

• Experience building highly scalable and robust data processes.

• Experience with cloud data warehouses: Snowflake (preferred), Redshift, BigQuery, or Azure Synapse, including performance tuning, partitioning, and clustering.

• API Integrations: Experience working with BULK and REST APIs to ingest data from third-party platforms and build robust integrations.

• Cloud Infrastructure Proficiency: AWS, GCP, or Azure, including services such as S3, Lambda, Glue, DataProc, DataFlow, or Cloud Functions.

• Data Orchestration & Automation Tools: Proficiency with Airflow, astronomer, or similar for automating and orchestrating ETL pipelines.

• Data Quality & Validation: Strong experience in building and automating data validation, quality checks, and error-handling pipelines.

Bonus Points:

• Marketing Automation Tools Knowledge: Experience with platforms such as Salesforce, Marketo, People.ai, Outreach  and CRM systems for data integration and processing.

• Experience with CI/CD pipelines for automating data pipeline deployments.

• Understanding of machine learning concepts: Ability to collaborate with data science teams and support machine learning initiatives through data preparation, transformation and F