Note: The job is a remote job and is open to candidates in USA. Sequoia Connect is a talent-focused technology ecosystem partnering with a global automation and digital transformation organization. The Analytics Engineering Manager will define analytics strategy, govern semantic and reporting layers, lead analytics teams, and optimize data workloads across Redshift, Athena, QuickSight, and Tableau. The role also focuses on data quality, governance, executive communication, and self-service analytics capabilities.
Responsibilities
- Define analytics strategy, KPI frameworks, and data product standards across teams
- Own the enterprise semantic/reporting layer strategy across QuickSight/Tableau and Redshift/Athena
- Partner with data engineering and architecture on domain models, data contracts, and self-serve analytics capabilities
- Establish and enforce data quality, documentation, and review processes; own escalation and resolution paths for analytics issues
- Lead and mentor analysts; manage capacity, prioritization, and quality across multiple workstreams
- Communicate insights and trade-offs to executives; drive decision-making with clear narratives and measurable outcomes
- Optimize cost and performance of analytical workloads; recommend storage/query patterns (e.g., Redshift vs. Athena vs. row-level caching)
- Ensure compliance with data governance, privacy, and security standards; steward access policies in coordination with platform teams
Skills
- Degree holders for the visa application process
- Leadership of analytics programs and teams; proven stakeholder and executive communication
- Mastery of SQL and performance engineering on Redshift; deep Athena/S3 expertise (partitioning, Parquet/ORC, Glue cataloging)
- Expert BI architecture and governance in QuickSight/Tableau, including semantic layers and certification processes
- Advanced Python for analytics automation, reproducibility, and standards
- Deep understanding of dimensional modeling, domain-driven design for analytics, and metric governance
- Strong grounding in data quality frameworks, documentation practices, and analytics SDLC (versioning, review, release)
- High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery
- Technologist DNA: A deep understanding of the difference between "coding" and "engineering."
- Experience influencing data platform architecture decisions; familiarity with Redshift Spectrum, RA3 architecture, concurrency scaling
- Exposure to cost optimization and workload management at scale; capacity planning and SLA definition
- Good understanding of using AI tools like Github Copilot or similar for code productivity
- Familiarity with cloud-native foundations or AI coding assistants
- Advanced Oral English: For seamless collaboration with global teams
- Advanced Spanish
Benefits
- Remote work arrangement
- Continuous learning and exposure to complex engineering challenges through collaboration within an international network of expertise
- Growth-focused workplace with opportunities to accelerate professional development
Company Overview