ML Data Engineer Jobs & Internships 2026
ML data engineers build the data pipelines and storage infrastructure that feed machine learning models with high-quality, well-structured training and serving data. The role sits at the intersection of traditional data engineering and the specialized requirements of ML systems — understanding not just how to move data efficiently but what properties of that data are critical for model training quality. As companies scale their ML operations, the data infrastructure layer has become a major bottleneck and a critical investment area.
What Does a ML Data Engineer Do?
ML data engineers design and implement batch and streaming ETL pipelines that collect, clean, and transform raw data from dozens of sources into curated datasets suitable for model training. They build labeling and annotation pipelines that route raw examples to human annotators and aggregate their labels into training-ready format with quality controls. Data versioning is a core responsibility — maintaining immutable, reproducible snapshots of training datasets so that model performance can be traced back to specific data versions. They implement data validation systems that catch schema changes, distribution shifts, and label inconsistencies before they corrupt training runs. Feature engineering infrastructure — computing and storing derived features that are used by multiple models — is another major area of ownership.
Required Skills & Qualifications
- ✓Apache Spark for large-scale distributed data transformation and feature computation
- ✓Apache Kafka and Flink for real-time streaming data pipelines
- ✓Data warehouse design in Snowflake, BigQuery, or Redshift for ML feature storage
- ✓Data quality frameworks: Great Expectations, dbt tests, and custom validation rules
- ✓Dataset versioning with DVC, Pachyderm, or LakeFS
- ✓SQL optimization for multi-terabyte analytical workloads
- ✓Python data engineering with Airflow for orchestration and pandas for transformation
- ✓Cloud storage patterns: partitioned Parquet on S3/GCS for efficient ML dataset access
A Day in the Life of a ML Data Engineer
Morning starts with reviewing the data quality dashboard — a validation check on the training dataset pipeline failed overnight because an upstream source changed its schema, dropping a critical feature column. After implementing a schema migration and running the backfill, you spend the late morning in a design review for a new real-time feature pipeline that will compute user engagement signals within one second of user actions. Post-lunch time is often spent working with annotation teams to improve the data collection pipeline for a new computer vision task, implementing quality control mechanisms that flag low-confidence annotations for re-review. The afternoon closes with optimizing a Spark job that processes daily training data — partitioning strategy changes reduce processing time by 40%.
Career Path & Salary Progression
Data Engineering Intern → ML Data Engineer I → Senior ML Data Engineer → Staff Data Engineer → Principal Data Architect
| Level | Base Salary | Total Comp (with equity) | Intern Monthly |
|---|---|---|---|
| Intern | — | — | $8,000–$12,000/mo |
| Entry-Level (0–2 yrs) | $115,000–$165,000 | +20–40% in equity/bonus | — |
| Mid-Level (3–5 yrs) | $165,000–$231,000 | +30–60% in equity/bonus | — |
| Senior (5–8 yrs) | $231,000–$323,000 | +50–100% in equity/bonus | — |
Salary data sourced from Levels.fyi, Glassdoor, and company disclosures. 2026 estimates.
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ML Data Engineer — Frequently Asked Questions
How does an ML data engineer differ from a traditional data engineer?
Traditional data engineers build pipelines for analytics and business intelligence. ML data engineers have additional requirements: understanding training vs. serving data splits, point-in-time correctness for feature computation, dataset versioning, annotation pipeline management, and the specific data quality requirements that affect ML model performance.
Is Databricks a good company to work at for ML data engineering?
Databricks is arguably the most important company for ML data engineering, having built Delta Lake, MLflow, and the Lakehouse architecture. Working at Databricks provides exposure to the infrastructure problems of hundreds of enterprise ML teams and exceptional depth in distributed computing. It's a top-tier employer for data engineering growth.
What is the difference between a feature store and a data warehouse for ML?
A data warehouse is optimized for batch analytical queries with complex joins. A feature store is optimized for serving precomputed features with low latency to online ML models while also providing point-in-time correct offline datasets for model training. Feature stores like Feast and Tecton serve both online and offline consumers from a single source of truth.
What certifications help for ML data engineering roles?
The Databricks Certified Data Engineer Associate and Professional certifications are highly relevant. The Google Professional Data Engineer and AWS Data Analytics Specialty certifications validate cloud-specific data engineering skills. dbt Fundamentals certification is useful for analytics engineering-adjacent roles.
How important is SQL vs. Python for ML data engineers?
Both are essential. SQL is the primary language for querying large datasets in warehouses like BigQuery and Snowflake. Python is used for pipeline orchestration, data transformation logic, and integration with ML frameworks. Spark expertise spans both languages. Senior ML data engineers are typically fluent in all three.