Job Description

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Job Responsibilities:

The Senior AI/ML Data Engineer is a hands-on technical contributor responsible for designing, building, and maintaining the data infrastructure that powers AI/ML product features across the organization. This includes building ETL pipelines between diverse data sources and destinations, architecting new solutions to support production-level APIs, syncing application data into analytics-ready warehouses, and tackling ad hoc data challenges that don't fit neatly into existing patterns. The scope of work varies from standing up repeatable sync jobs to researching and prototyping new approaches for problems the team hasn't solved before.

The ideal candidate is someone who thrives in a fast-moving product engineering environment, can navigate ambiguity in requirements, take ownership of problems from investigation through production deployment, and maintain a high standard of documentation and data quality. They should be equally comfortable maintaining established pipelines and independently researching new tools or architectures.

Key Responsibilities

• Data Pipeline Development: Design, build, and maintain ETL pipelines that move data between diverse sources and destinations, including application databases, cloud data warehouses, third-party APIs, and object storage.

• Cross-Functional Collaboration: Partner with product managers, research scientists, QA, and platform engineers to translate business and ML requirements into data engineering work with clear acceptance criteria and documentation.

• Data Quality & Investigation: Investigate and resolve data integrity issues surfaced by stakeholders, including missing data, incorrect mappings, duplicates, and schema mismatches.

• Data Security & Compliance: Ensure data security, quality, and compliance standards are met across all data pipelines and processes.

• Automation: Automate the end-to-end process of data ingestion, transformation, and processing to increase efficiency and reliability.

• Dataset Management Develop and maintain complex datasets while ensuring a uniform design and engineering methodology across various systems.

• Mentorship: Mentor junior engineers, conduct peer reviews using documented checklists, and identify opportunities to improve team processes and adopt new tooling.

Qualifications

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• Experience: 4-6 years of experience in data engineering or related fields, with a focus on AI/ML.

• Technical Proficiency:

  • Strong Python skills, including experience with migration frameworks (e.g., Alembic, SQLAlchemy), and production-grade data processing scripts.

  • Advanced SQL across both transactional databases (PostgreSQL or similar) and columnar data warehouses (AWS Redshift strongly preferred).

  • Proficiency in cloud services (e.g., dbt, AWS, Azure, GCP).

  • Proficiency with AWS services: Redshift, S3, Lambda, IAM.

  • Strong Git and GitHub skills, including branching strategies, pull request workflows, and code review practices.

  • Expertise in building scalable, fault-tolerant data pipelines.

• Problem-Solving Skills: Excellent problem-solving and analytical skills, with the ability to identify and address performance bottlenecks.

• Leadership Skills: Experience mentoring junior engineers or leading small teams, with strong technical leadership capabilities.

• Collaboration Skills: Excellent communication and teamwork skills, with the ability to work effectively in cross-functional teams.

Soft Skills

• Comfortable with Ambiguity: Thrives when requirements are incomplete or evolving. Able to independently research the best path forward rather than waiting for a perfect specification.

• Ownership & Accountability: Takes full ownership of problems from investigation through resolution. Operates with a startup mentality. Identifies issues, drives solutions forward independently, and isn't afraid to introduce new approaches while respecting what already works.

• Stakeholder Communication: Communicates clearly with both technical and non-technical stakeholders. Comfortable reaching out directly to gather requirements or unblock work, and knows when and to whom to escalate.

• Proactive Problem Solving: Anticipates issues before they become blockers. Surfaces data quality problems, dependency risks, and process gaps without being asked.

• Collaboration & Team Orientation: Works effectively across time zones and cross-functional teams. Contributes to team knowledge through documentation, knowledge sharing, and thorough reviews.

Additional Job Details: