Posted in Other about 3 hours ago.
Location: Sunnyvale, California
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.
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.
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 Feature store support.
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