Note: The job is a remote job and is open to candidates in USA. HMH is a division of Houghton Mifflin Harcourt that supports students and educators through various educational services. The Senior Analytics Engineer will work across multiple engineering teams to design, develop, and maintain data systems, focusing on machine learning models and data pipelines.
Responsibilities
- Model raw data into clean, tested, and reusable datasets, making it easier for other stakeholders to view and understand data in a data warehouse or database. Since data models are created around business needs, the job of analytics engineers is to define the rules and requirements for the formats and attributes of data
- Translate user and product requirements into data model requirements to execute against and make critical decisions regarding the business rules and how they’re implemented
- Builds ETL pipelines that can efficiently process very large datasets
- Design, implement and maintain online and offline feature stores to support ML training and inference. Senior Analytics Engineers will be responsible for managing low latency (online) and high latency (offline) systems
- Develop and maintain data and design documentation to ensure that everyone on the team uses the same definitions and language and is executing against the same architectural vision. This involves providing identifiable and understandable descriptions of data and data system components as well as exposing them in a way for all consumers to easily comprehend. Senior analytics engineers create design and data documents and utilize them to communicate effectively with stakeholders and drive innovation
- Draft and maintain documents that describe how the data flows from data sources to consumption by visualizing them with directed acyclic graphs (DAGs). From a technical user perspective, the lineage helps them to determine the root cause of an error in the whole data flow
- Define metrics and implement tests to guarantee data meets operational and analytics needs. Responsible for implementing data quality standards —how data should be formatted, shown, and used across the organization
- Develop and maintain automation, scheduling and monitoring of processes designed to gather data from disparate sources and preparing them for data analysis
- Use CI/CD processes throughout the data model development lifecycle to develop higher quality code and data models without disruption to production
Skills
- Over 4 years of hands-on experience in data engineering, analytics, or data science, with a strong focus on supporting data pipelines for machine learning models deployed in production environments
- Bachelor's degree in statistics, mathematics, computer science, software engineering, or related field. Master's degree is a plus
- Proficient in SQL and Python
- Practical experience to handle various data orchestration tasks is required
- Data modeling: Experience developing data models for specific business processes. Familiarity with common data modeling techniques including Star Schema (Kimball's), One Big Table (OBT) and Data Vault
- Extensive hands-on experience with tools for building data pipelines like Snowflake, Amazon Redshift, and Google BigQuery; ETL tools like AWS Glue, Talend, or others; Business Intelligence tools like Tableau, Looker, or equivalent
- Comfortable with software engineering best practices: version control (git), writing unit testing, code review, and CI/CD
- Demonstrates exceptional interpersonal and communication skills, facilitating seamless collaboration throughout the organization. Proficient in understanding and anticipating stakeholder needs, effectively engaging with key stakeholders to convey the value of analytics initiatives and align them with business objectives. Committed to fostering and maintaining positive, productive relationships with colleagues and customers
- Experience with the ML lifecycle is preferred, in particular feature stores
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