
Senior Data Engineer
2 days ago
We're seeking a seasoned data engineer to spearhead our analytics team's growth and development.
This strategic position requires someone with extensive experience in handling log-level data, cross-platform engineering, and proficiency in modern business intelligence tools.
The ideal candidate will play a pivotal role in designing and implementing scalable data pipelines, driving analytics strategy, and mentoring junior analysts.
Key Responsibilities:- Team Leadership and Mentoring: Lead and guide a team of data analysts, ensuring high-quality delivery and fostering technical upskilling.
- Data Pipeline Development: Design, develop, and maintain robust ETL/ELT pipelines using Google Cloud Platform tools.
- Log-Level Data Ingestion: Process log-level data from platforms like Google Ad Manager, Google Analytics, DV360, and other advertising and marketing technology sources.
- Data Integration: Build and optimize data pipelines from diverse sources via APIs, cloud connectors, and third-party tools.
- Data Management: Integrate and manage data across multiple cloud platforms and data warehouses.
- Data Modeling: Own the creation of data models, data marts, and analytical layers to support dashboards and deep-dive analyses.
- Dashboards and Reporting: Build and maintain scalable, intuitive dashboards using Looker Studio, Tableau, Power BI, or Looker.
- Collaboration: Partner with engineering, product, revenue operations, and client teams to gather requirements and drive strategic insights from data.
- Data Governance: Ensure data governance, security, and quality standards are followed across the analytics ecosystem.
Requirements:
- A minimum of 4-6 years of experience in data analytics or data engineering roles, with at least 1-2 years in a leadership capacity.
- Deep expertise working with log-level AdTech data—Google Ad Manager, Google Analytics, GA4, programmatic delivery logs, and campaign-level data.
- Strong knowledge of SQL and Google BigQuery for large-scale data querying and transformation.
- Hands-on experience building data pipelines using GCP tools.
- Proven experience integrating data from various APIs and third-party connectors.
- Experience working with multiple data warehouses: Snowflake, DOMO, AWS Redshift, etc.
- Strong skills in data visualization tools: Looker Studio, Tableau, Power BI, or Looker.
- Excellent stakeholder communication and documentation skills.
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