Engineering Lead, Analytics

Kindsight

1d•$150,000 - $170,000

About The Position

We are seeking a highly skilled Engineering Lead, Analytics to design, build, and lead our data warehousing, analytics, and business intelligence capabilities across Kindsight’s product suite. In this role, you will contribute deep technical expertise in data architecture, ETL/pipeline development, and data visualization while providing strategic insight to drive data-driven innovation. You will collaborate closely with cross-functional teams—spanning product, engineering, data science, and business stakeholders—to deliver scalable, production-grade analytics solutions. You will hire, lead, and grow a high-performing team of data engineers and analysts, including offshore team members, while remaining a hands-on technical leader comfortable with rapid prototyping and agile delivery.

Requirements

6+ years of experience in software engineering with 1+ years in engineering management.
3+ years of core data warehousing experience including architecture, data modeling, and optimization.
Must have hands-on experience with dimensional modeling, star/snowflake schemas, and platforms such as Redshift, Snowflake, or BigQuery.
3+ years building scalable, automated ETL/ELT data pipelines with tools like AWS Glue, Airflow, dbt, or Spark.
Must have experience handling data ingestion, transformation, and automated data quality solutions.
3+ years of data visualization and BI experience using Tableau, AWS QuickSight, or equivalent.
Must have demonstrated experience in dashboard design, KPI definition, and interactive reporting.
3+ years of cloud architecture proficiency (AWS preferred) including S3, Redshift/Athena, Glue, IAM/security, serverless setups, and cost/performance optimization.
3+ years of advanced SQL proficiency and hands-on experience with descriptive, predictive, and prescriptive analytics, KPI development, and data-driven decision support.
Bonus for statistical fundamentals or close collaboration with data analysts.
2+ years of experience hiring and leading offshore or distributed engineering teams, managing delivery and agile processes, performing code reviews, and ensuring knowledge transfer.
2+ years demonstrating rapid prototyping and an agile mindset, including proof-of-concept development, CI/CD practices, and early adoption of test automation.
2+ years of experience with data security, governance, and compliance, including data encryption, access controls, and audit frameworks.
Bachelor’s or Master’s degree in Computer Science, Data Science, Analytics, or a related field.
Excellent problem-solving skills and the ability to communicate complex technical concepts to both technical and non-technical audiences.

Nice To Haves

Experience building within the Salesforce platform, including API integrations, custom ETL with Salesforce data models and objects.
Familiarity with CRMs, ERPs, or launching data products on Salesforce.
Familiarity with fundraising practices and metrics (e.g., donor lifetime value, conversion rates).
Contributions to open-source data engineering or analytics projects.

Responsibilities

Design, build, and optimize the company’s data warehouse architecture using dimensional modeling (star/snowflake schemas) on platforms such as Redshift, Snowflake, or BigQuery.
Develop and maintain scalable, automated ETL/ELT pipelines using tools like AWS Glue, Apache Airflow, dbt, and Spark to support data ingestion, transformation, and quality assurance.
Create and manage interactive dashboards, KPI frameworks, and reporting solutions using Tableau, AWS QuickSight, or equivalent BI platforms.
Architect and manage cloud-based data infrastructure on AWS, including S3, Redshift/Athena, Glue, IAM/security configurations, serverless setups, and cost/performance optimization.
Lead end-to-end development of analytics features and the supporting backend infrastructure required to deliver them into production.
Translate business and product requirements into scalable data and analytics solutions, leveraging descriptive, predictive, and prescriptive analytics approaches.
Hire, lead, and mentor distributed and offshore engineering teams, managing delivery through agile processes, code reviews, and structured knowledge transfer.
Drive rapid prototyping and proof-of-concept development, championing CI/CD pipelines and test automation to accelerate iteration cycles.
Implement and enforce data security, governance, and compliance standards including encryption, access controls, and audit practices.
Partner with product, engineering, and data science teams to integrate analytics features into production systems with reliability, efficiency, and scalability.
Stay current on emerging trends, tools, and best practices in data engineering, analytics, and cloud architecture to drive innovation within the organization.