Our Firm



Big Data Engineer I


MINDBODY | Product Development


MINDBODY is poised to lead predictive analytics in the Enterprise Fitness, Wellness & Beauty industries are with its unique suite of products enhanced by Machine Learning and Artificial Intelligence. Our goal is to enable a completely data-driven, automated analytics platform to enrich our customers' existing apps and workloads for a more efficient and productive Fitness, Wellness & Beauty provider ecosystem.


As a Big Data Engineer I, you will provide data engineering platform and database tools, coding execution and delivery of Data Layer for Data Science (Machine Learning & Artificial Intelligence) core engines, focused on large, industry-scale B2B and B2C data sets, working with lead engineers.  Automation towards integrating Robotics is a core aspect of this vision.  Working as a part of the product team, this Big Data Engineer I will be expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. This role is for an early career data pipeline builder and data wrangler with ML/AI interests, who enjoys optimizing data systems and building them from the ground up. The Big Data Engineer I supports our ML software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. This role works alongside a product owner, technical lead, and team of software developers and data engineers to support delivery of high-value analytics, Machine Learning, Artificial Intelligence and software products for Fitness, Wellness & Beauty Vendor Partners worldwide.

Skills & Requirements


  • 2+ years of experience in Data Science/Machine Learning with Data Engineering emphasis
  • Bachelor’s Degree in Computer Science, Engineering, Mathematics, Applied Sciences, Statistics or other job-related discipline or equivalent experience required
  • Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field preferred
  • Advanced working SQL knowledge and experience working with relational databases, query authoring as well as working familiarity with a variety of databases.
  • Experience building and optimizing ‘big data’ data pipelines, architectures and data sets
  • Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement
  • Strong analytic skills related to working with unstructured datasets
  • Build processes supporting data transformation, data structures, metadata, dependency and workload management
  • A successful history of manipulating, processing and extracting value from large disconnected datasets
  • Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores
  • Strong project management and organizational skills
  • Experience supporting and working with cross-functional teams in a dynamic environment
  • Experience with big data tools: Hadoop, Spark, Kafka, etc.
  • Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
  • Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
  • Experience with AWS cloud services: EC2, EMR, RDS, Redshift
  • Experience with stream-processing systems: Storm, Spark-Streaming, etc.
  • Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.



  • Provides hands-on execution and implementation of data science models
  • Works under general supervision to identify and analyze problems
  • Weighs relevance and accuracy of information
  • Uses professional, fundamental concepts, practices and procedures of the artificial intelligence and machine learning discipline
  • Uses data to support own point of view when interacting with the team
  • Provides information, analysis and recommendations in support of the machine learning team
  • Takes actions that are consistent with goals and objectives
  • Creates and maintains optimal data pipeline architecture
  • Assembles large, complex data sets that meet functional / non-functional business requirements
  • Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies
  • Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics
  • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs
  • Keep our data separate and secure across national boundaries through multiple data centers and AWS regions
  • Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
  • Work with data and analytics experts to strive for greater functionality in our data systems
  • All other duties as assigned