Senior Data and Analytics Engineer 20

Overview

At Leadership Circle, we are reimagining how leadership grows from one-time events to personalized, continuous, human-centered experiences.

We are seeking a Senior Data and Analytics Engineer to help build the data foundation that powers our next-generation platform.

Thisis a new and critical function within our engineering organization, and the person in this role will have the opportunity to shape our data strategy from the ground up.

This role sits at the intersection of data engineering, analytics, and software development.

You will design and build data pipelines, models, and schemas that enable user-facing analytics features, internal dashboards, and AI/ML experimentation.

You will also evaluate and recommend cloud-based data platforms and tools such as Snowflake, Databricks, or BigQuery to power our data warehouse and data products.

You will partner closely with product, engineering, design, and data science stakeholders to deliver reliable, well-documented, and scalable data solutions.

Success in this role requires hands-on technical depth, strong analytical thinking, and the ability to establish best practices in an environment where much of this work is being done for the first time.

A core expectation of this role is the active and effective use of AI-assisted development tools such as Claude Code.

Success in this role requires embracing AI as a fundamental part of how data systems are designed, built, and maintained, using it to accelerate development, improve code quality, and increase overall engineering effectiveness.

Responsibilities

Data Engineering & Analytics

Build, maintain, and optimize data pipelines, models, and schemas in collaboration with data scientists and platform engineers

Design and implement a scalable data warehouse architecture to support both customer-facing analytics and internal reporting needs

Evaluate and recommend cloud-based data platforms such as Snowflake, Databricks, or BigQuery to power data products and the internal data warehouse, establishing foundational infrastructure

Develop and maintain data transformations that ensure clean, reliable, and well-documented datasets for downstream consumers

Implement data quality checks, monitoring, and alerting to ensure pipeline reliability and data integrity

Data Products & Tooling

Partner with product, engineering, and design to define and deliver user-facing analytics features

Develop tools, dashboards, data products and models to support both customer needs and internal insight

Support AI and ML experimentation by providing clean, reliable, and well-documented data to data scientists and engineers

Create and maintain documentation, data dictionaries, and lineage tracking to ensure organizational understanding of data assets

Best Practices & Governance

Help establish best practices for analytics engineering including testing, observability, and data governance

Define and enforce data modeling standards, naming conventions, and schema design principles

Contribute to engineering best practices including CI/CD for data pipelines, version control for transformations, and automated testing of data models

Cross-Functional Collaboration Serve as a key member of cross-functional team spanning Product, UX, engineering, and other departments, partnering closely to understand data needs, align on priorities, and iteratively deliver analytics capabilities that serve both the product organization and the broader business

Communicate technical concepts clearly and effectively to both technical and non-technical stakeholders

Mentor other engineers on data best practices through code reviews, pairing, and knowledge sharing

Foster a collaborative team environment by communicating proactively, sharing context, asking questions openly, and contributing to thoughtful technical discussions

AI-Assisted Development

Actively use AI-assisted development tools such as Claude Code as a core part of daily engineering work including code generation, data modeling, debugging, and system comprehension

Continuously improve how AI tools are used within the team to increase development speed, reduce friction, and improve overall engineering effectiveness

Requirements 7 or more years of experience in data engineering, analytics engineering, or software engineering with a strong data focus

Proven experience designing and building data warehouses, data lakes, or similar analytical data stores from the ground up

Demonstrated experience in partnering with Product and UX to bring analytics products to market

Deep proficiency with SQL and experience with modern data transformation frameworks such as dbt, Apache Spark, or similar tools

Strong experience with cloud data platforms and services such as AWS Redshift, Snowflake, BigQuery, Databricks, or similar technologies

Experience building and maintaining ETL/ELT pipelines using tools such as Airflow, Dagster, Prefect, Fivetran, or similar orchestration platforms

Proficiency in Python or Typ