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Data Architect, Lead Engineer
Posted 5 days agoViewed
💎 Seniority level: Lead, 8+ years
🏢 Company: Servant👥 11-50ConsultingAdviceProfessional Services
⏳ Experience: 8+ years
Requirements:
- 8+ years of experience in data architecture, data engineering, or related fields, with a strong track record of designing and implementing scalable data solutions.
- Deep expertise in data modeling, performance tuning, and query optimization.
- Strong SQL and Python skills for developing and optimizing ETL/ELT workflows.
- Experience with modern data warehouses (e.g., Snowflake, BigQuery, Redshift, Databricks), but with the ability to apply general best practices across platforms.
- Expertise in ETL/ELT tools such as Fivetran, Stitch, dbt, or Airflow.
- Strong understanding of data quality, lineage tracking, and governance best practices.
- Experience with cloud platforms (AWS, GCP, Azure) and related services (e.g., S3, Data Lakes, IAM security).
- Excellent problem-solving skills—able to balance best practices with real-world constraints and deliver solutions quickly.
- Collaborative mindset with the ability to work across teams and influence key decisions.
Responsibilities:
- Design and implement a scalable, high-performance, and secure data architecture that supports operational reporting, advanced analytics, and business intelligence.
- Integrate diverse data sources (e.g., Shopify, Funraise, Braze) into a unified architecture, ensuring seamless data flow across systems.
- Continuously assess and refine data models and infrastructure to align with evolving business needs and long-term objectives.
- Provide creative, efficient solutions for data challenges, balancing best practices with pragmatic decision-making.
- Lead the migration and modernization of data platforms, ensuring minimal downtime and data integrity.
- Guide data modeling, performance tuning, and storage optimization, ensuring scalability and efficiency.
- Establish best practices for ETL/ELT workflows, balancing automation, monitoring, and fault tolerance.
- Work with cloud-based data platforms (AWS, GCP, Azure) to design robust and cost-effective data solutions.
- Stay informed on emerging technologies and recommend improvements to the data ecosystem.
- Implement frameworks for data validation, monitoring, and anomaly detection to ensure reliability and trustworthiness.
- Document data lineage, metadata, and governance policies to promote transparency and usability.
- Develop comprehensive technical documentation for data architecture, pipelines, and governance processes.
- Assess the cost implications of implementing and maintaining data solutions, considering factors such as infrastructure, licensing, operational expenses, and staffing requirements. Provide a holistic view of the total cost of ownership (TCO) for proposed architectures, ensuring solutions are scalable, efficient, and cost-effective.
- Collaborate with stakeholders to balance performance, security, and budgetary constraints while optimizing the long-term value of data investments.
- Act as a technical mentor and leader, guiding data engineers in architectural decisions and best practices.
- Work closely with business stakeholders to translate complex data requirements into practical, scalable solutions.
- Serve as a bridge between technical and non-technical teams, communicating ideas clearly and effectively.
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