Software Engineering at HMH is focused on building fantastic software to meet the challenges facing teachers and learners, enabling and supporting a wide range of next generation learning experiences
We design and build custom applications and services used by millions
We are creating teams full of innovative, eager software professionals to build the products that will transform our industry
We are staffing small, self-contained development teams with people who love solving problems, building high quality products and services
We use a wide range of technologies and are building up a next generation microservices platform that can make our learning tools and content available to all our customers
If you want to make a difference in the lives of students and teachers and understand what it takes to deliver high quality software, we would love to talk to you about this opportunity

About the Role:

HMH Data Engineer is a senior position with expectations for both technical leadership, and a force to grow and sustain HMHs positive, high energy technology company culture
The Senior Data Engineer creates high-quality craftsmanship-level software for HMH clients and internal users, and are expected to be experts in the software languages used
or required by HMH
This includes meeting all requirements/specifications, testing/QA requirements, methodologies and other HMH software/product requirements
This position must be adaptable to changing technology environments, and work collaboratively with all participants in a software development project
The Senior Data Engineer may be called upon to fulfill functions related to software development that are not specifically software programming, such as requirements gathering, producing documentation, quality assurance testing, mentoring other developers, and leading projects
This position provides technical guidance and serves as a mentor/resources to the more junior level Software Engineer roles

Key Responsibilities:

Design, build, and maintain ETL/ELT data pipelines from diverse data sources (databases, APIs, event streams, files).
Develop and manage data warehouse/lake solutions (e.g., Snowflake, BigQuery, Redshift, Databricks, or similar).
Implement and maintain data quality checks, validation, and monitoring to ensure high data reliability.
Optimize queries and pipelines for performance, scalability, and cost efficiency.
Collaborate with stakeholders to understand data needs and translate them into technical solutions.
Maintain documentation of data models, pipelines, and systems.
Implement and follow data governance, security, and privacy standards (e.g., access control, PII handling).
Participate in code reviews, design discussions, and continuous improvement of data engineering standards and tooling.
Troubleshoot and resolve data-related issues in production environments.

Required Qualifications:

Bachelors degree in Computer Science, Engineering, Information Systems, Mathematics, or equivalent practical experience.
Strong experience with SQL (complex joins, window functions, performance tuning).
Professional experience with at least one programming language used for data engineering (e.g., Python, Scala, or Java), preferably Python.
Hands-on experience with ETL/ELT tools or frameworks (e.g., Airflow, dbt, Luigi, Kafka Streams, Flink, custom pipelines).
Experience with relational databases (e.g., PostgreSQL, MySQL, SQL Server) and working with large datasets.
Experience on at least one cloud platform (AWS, GCP, or Azure) and its data services (e.g., S3/GCS/ADLS, Redshift/BigQuery/Synapse, EMR/Dataproc).
Understanding of data modeling, warehousing, and orchestration concepts.
Familiarity with version control (Git) and CI/CD practices for data code.
Strong problem-solving skills and ability to work with incomplete or ambiguous requirements.
Good communication skills and ability to work collaboratively in cross-functional teams.

Preferred Qualifications (Nice to Have):

Experience with dbt or similar transformation frameworks.
Familiarity with BI/analytics tools (e.g., Tableau, Power BI, Looker, Mode).
Exposure to data governance and catalog tools (e.g., Collibra, Alation, Amundsen, DataHub).
Experience working with data science / ML teams and supporting ML feature pipelines.
Knowledge of software engineering best practices (testing, code quality, observability).

Tools & Technologies:

Languages: SQL, Python, Javascript
IAC: Terraform Orchestration: dbt
Warehousing/Lake: Snowflake
Storage: S3
Streaming: Pub/Sub
Infra/DevOps: Docker, GitHub/GitLab, CI/CD

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