Lead Data Engineer

About Leapfrog

Leapfrog is on a mission to be a role model technology company. Since 2010, we have relentlessly worked on crafting better digital products with our team of superior engineers. We’re a full-stack company specializing in SaaS products and have served over 100 clients with our mastery of emerging technologies.

We’re thinkers and doers, creatives and coders, makers and builders— but most importantly, we are trusted partners with world-class engineers. Hundreds of companies in Boston, Seattle, Silicon Valley, and San Francisco choose us to gain speed, agility, quality, and stability, giving them an edge over their competitors.

About the role

As a Lead Data Engineer, you will closely work with to build data processing, data warehousing infrastructure, and data visualization systems. You will be part of a learning culture where teamwork and collaboration are encouraged, excellence is rewarded, and diversity is respected and valued.

• Follow software development best practices and industry standards to deliver a robust system

• Write analytical queries based on business requirements

• Database schema design based on the product requirements

• Database query optimization, maintain ETL workflows and SQL scripts for data transformation and analysis

• Engage and participate in all stages of the Software Development life cycle process: Planning, design, development, testing and deployment.

• Prepare flow diagram and technical documentation

• Work and collaborate independently or in a team

• 5+ years of experience in software development with strong proficiency in relational databases and SQL (PL/SQL, T-SQL preferred)

• Hands-on experience with at least one programming language such as Python or JavaScript

• Strong understanding of data structures, algorithms, design patterns, and architectural principles

• End-to-end understanding of system architecture including frontend, backend, REST APIs, OLTP and OLAP systems

• Experience designing and working with OLTP and OLAP databases, including data warehousing and data modeling best practices

• Proven experience building and maintaining production-grade data pipelines (ETL, ingestion, CDC/event-driven architectures)

• Strong hands-on experience with data transformation, stored procedures, triggers, and database functions

• Experience with orchestration tools like Apache Airflow (or similar)

• Experience working with modern data stack tools such as Snowflake (including medallion architecture) and DBT (models, incremental logic, macros)

• Familiarity with data quality, observability, and alerting practices

• Experience with cloud platforms such as AWS, Azure, or Google Cloud, and building cloud-native applications

• Working knowledge of CI/CD practices and Git-based development workflows

Preferred / Nice-to-Have Skills

• Experience with AWS services such as EC2, S3, RDS, Lambda, SNS, EBS, and Fargate

• Familiarity with Infrastructure as Code tools like Terraform

• Experience with containerization tools such as Docker and Kubernetes

• Exposure to ETL automation tools like Airflow, Pentaho DI, or SSIS

• Knowledge of data visualization tools such as Power BI or Tableau

• Experience with Generative AI and Large Language Models (LLMs) in real-world applications

• Basic understanding of data science concepts

Soft Skills

• Strong problem-solving and analytical thinking

• Clear and effective communication, especially around data trade-offs and issues

• Strong documentation practices and attention to detail

• Ability to work in an agile environment and manage multiple contexts

• Collaborative mindset with experience working closely with engineers, product teams, and stakeholders

• Willingness to learn, take ownership, and grow within the organization