Senior Data Engineer (Remote)

Neumo Holdings LLC• TX, US, TX

1d•Remote

About The Position

Our Revenue Compliance platforms generate a large and growing volume of tax, licensing, and compliance data. Today, much of the work of moving, reconciling, and reporting on that data depends on legacy tooling and manual steps. The Data Engineer designs and builds the pipelines and data models that our reporting, analytics, and emerging AI initiatives depend on, and retires the manual and legacy processes those functions rely on today. The role also supports the data side of our migration to Amazon Aurora, ensuring that downstream reporting and extracts move cleanly as source systems change. This is a foundational position with wide scope and real constraints, suited to an engineer who wants to define how a data platform gets built rather than maintain an existing design.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field; equivalent professional experience will be considered in lieu of a degree.
Four to eight years of data engineering experience, including production ownership of pipelines the candidate personally designed and built.
Demonstrated experience designing and operating ETL and ELT pipelines, including orchestration, scheduling, error handling, and retry logic.
Hands-on experience with a cloud data platform. AWS preferred; Azure or GCP considered.
Data warehouse and dimensional modeling experience sufficient to defend schema design decisions.
Experience applying version control and CI/CD practices to data work, not solely to application code.
Experience with AWS-native data tooling such as Glue, DMS, S3, Redshift, Athena, Lambda, or Step Functions.
Experience with pipeline orchestration frameworks such as Airflow, Dagster, or Prefect, and with transformation tooling such as dbt.
Experience migrating reporting and analytics workloads alongside a database platform migration.
Experience replacing legacy or low-code data tooling such as Alteryx or SSIS with engineered pipelines.
Experience preparing data foundations for machine learning or AI use cases.
Background in tax, financial services, government technology, or another regulated, audit-sensitive domain.
Experience with data governance, lineage, and cataloging practices.
Advanced SQL, including complex joins, window functions, and query optimization, together with the judgment to recognize when a query is the wrong tool.
Strong Python for data engineering, using pandas, SQLAlchemy, or equivalent libraries, and general scripting proficiency.
Working knowledge of relational database platforms such as MySQL, MariaDB, SQL Server, or PostgreSQL.
Knowledge of dimensional modeling and data warehouse design principles.
Ability to translate ambiguous business questions into concrete, testable data requirements.
Ability to design and defend technical approaches independently, and to make architectural decisions with limited supervision.
Clear written communication, including the ability to produce documentation that others can rely on without follow-up.
Ability to collaborate effectively with engineering, reliability, and business stakeholders in a fully remote environment.
Knowledge of data quality, reconciliation, and observability practices for production pipelines.
Ability to mentor other engineers and establish shared standards across a team.

Responsibilities

Design, implement, and operate ETL and ELT pipelines that move data from transactional systems into analytical and reporting environments on schedule, with monitoring and alerting sufficient to establish confidence in the results.
Replace legacy and manual data processes with maintainable, version-controlled, tested pipelines, reducing the number of steps that depend on an individual remembering a procedure.
Design warehouse schemas and data models that make reporting consistent across products rather than requiring each report to define its own logic.
Support the Amazon Aurora migration by ensuring downstream pipelines, extracts, and reporting move cleanly as source systems migrate, and by validating data integrity through cutover.
Build validation, reconciliation, and alerting into pipelines so that data quality issues are identified before a customer or auditor encounters them.
Partner with architecture and AI initiatives to prepare clean, well-structured, and well-documented datasets that enable downstream analytical and machine learning work.
Work with application engineers, the database administrator, site reliability, and business stakeholders to establish what the data means, not solely where it resides.
Maintain clear documentation of data sources, lineage, and business definitions.
Translate ambiguous business questions into concrete data requirements, and exercise independent judgment in selecting the appropriate technical approach.
At the senior level, mentor other contributors and raise the overall data engineering practice of the team.
Perform other duties as assigned.