Data Engineer
About the role
About the Role
Data Engineer focused on AI/ML
Your Area of Focus
Design, build, and maintain
scalable data pipelines for feature extraction, training data generation, and model monitoring.
- Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies.
- Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness.
- Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation.
•
Build reusable data engineering components
, including automated data quality checks, schema validation, and testing frameworks.
- Translate business requirements into scalable data solutions that enable analytics and machine learning use cases.
•
Optimize SQL queries, Spark workloads, and data processing pipelines
to improve performance and scalability.
- Collaborate with
ML Engineers
and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility.
- Break down technical work into manageable tasks and deliver high-quality solutions within an agile team.
Your Professional Qualifications
3+ years
of professional experience in Data Engineering, Software Engineering, or a related field.
- 2+ years of hands-on experience building and maintaining production data pipelines supporting analytics, reporting, or machine learning workloads.
- Strong proficiency in
Python
and SQL with experience developing production-quality data pipelines.
- Experience with modern data processing technologies such as
Spark, Airflow, Kafka,
or similar distributed data platforms.
- Experience working with cloud-based data platforms such as
Databricks
Snowflake
Delta Lake
lakehouse
technologies.
- Understanding of data modeling, data warehousing, and data governance best practices.
- Familiarity with machine learning data workflows, including training datasets, feature engineering, and data quality concepts.
- Experience deploying and supporting production data pipelines with monitoring, testing, and
CI/CD
practices.
- Strong problem-solving skills, attention to detail, and the ability to collaborate effectively across engineering and product teams.
- Excellent communication skills and a desire to continuously learn new technologies and engineering practices.
We are dedicated to attracting and retaining top talent with competitive and fair compensation. For this position, this range reflects our Zone 1 (National Average) pay band. Your specific compensation is thoughtfully determined by your experience, qualifications, the specific requirements of the role, and your Geo Zone. Our geo-zone system ensures your pay is competitive for your location, recognizing varying costs of labor across regions.
Our four geo zones are designed to reflect this:
Zone 2: Moderately Higher Cost Regions
Zone 4: Lower-Cost Regions
About Tebra
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Who can apply
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