Lead Analytics Engineer

Sequoia Connect•Atlanta, GA

11h•Remote

About The Position

At Sequoia Connect, we are a Talent-First Technology Ecosystem that redefines how elite professionals interact with the global digital landscape. We move beyond traditional models to act as a catalyst for the top 1% of global talent, connecting human potential with complex industrial execution. By joining our inner circle, you are not simply taking a position; you are aligning with a strategic partner dedicated to updating your "Human OS" and accelerating your growth through world-class, high-impact projects. We are currently partnering with a rapidly growing, automation-led powerhouse that serves 31 Fortune 500 companies across the financial, healthcare, and manufacturing sectors. With a global workforce of over 32,000 employees and a presence in 28 countries, our client is a titan of digital transformation. Their "Automate Everything, Cloudify Everything" strategy ensures you will be working at the absolute forefront of AI-driven automation and cloud solutions. This is your chance to thrive in a "Customer Success, First and Always" environment that prizes continuous learning and radical ownership. You will collaborate within an international network of expertise across 39 delivery centers worldwide, gaining exposure to complex engineering challenges that redefine industrial standards. If you are a driven professional looking for a dynamic, forward-thinking workplace where your growth is the priority, this is where you belong. We are currently searching for a Lead Analytics Engineer:

Requirements

Degree holders for the visa application process.
Expert-level proficiency in SQL on RDS, Oracle, and Redshift, including deep query tuning and vacuum/analyze awareness.
Proven expertise in AWS Athena, specifically regarding partitions, file formats, and cost control strategies.
Advanced Business Intelligence (BI) development skills with a strong emphasis on data storytelling and UX best practices for dashboards.
Strong Python programming skills focused on analysis, automation, packaging, and the reusability of common analytics functions (light use of pandas, statsmodels, scikit-learn).
Solid foundation in statistics tailored for inference and experiment design, with practical application in business contexts.
Demonstrated ability to translate complex business needs into robust analytical solutions using collaboration tools like Confluence and Jira.
High-Performance Mindset: Resilience, emotional intelligence, and a focus on agile delivery.
Technologist DNA: A deep understanding of the difference between "coding" and "engineering."
Advanced Oral English: For seamless collaboration with global teams.
Advanced Spanish.

Nice To Haves

Experience with advanced modeling tools and the governance of analytics layers.
Familiarity with AWS Glue Data Catalog, IAM basics for data access, and S3 file formats (Parquet/ORC).
Exposure to event-based data architectures (Kinesis, SNS, SQS) and data freshness SLAs.
Ability to build scalable, reusable dashboards and data models in QuickSight or Tableau while enforcing consistency and naming standards.
Familiarity with cloud-native foundations or AI coding assistants, such as GitHub Copilot or similar tools for code productivity.

Responsibilities

Lead the design and governance of core KPIs, metric definitions, and semantic layers for Our Client.
Deliver complex analytical work utilizing advanced SQL, focusing on performance tuning across RDS, Oracle, and Redshift, alongside cost-conscious querying in Athena.
Utilize Python to create reproducible analyses, automation scripts, and robust data validation checks.
Define data quality expectations, implement automated checks and anomaly detection, and triage resolutions collaboratively with engineering teams.
Conduct comprehensive deep-dive analyses, cohorting, funneling, forecasting, and experiment design.
Create and maintain Source-to-Target Mapping (STTM) documents through tight coordination with upstream and downstream stakeholders.
Improve query performance continuously through sort keys, distribution keys, partitioning, predicate pushdown, and compression awareness.
Establish strict version control and peer-review processes for analytics assets, contributing to lightweight CI for SQL and tests.