Machine Learning Engineer (DE)

Phx Fwd; Delaware; Presencial; USD 120.000 - 170.000; Jornada completa

Descripción de la vacante

Phx Fwd is seeking a data/ML data bridge to ensure high-quality, scalable data flows for model training, inference, evaluation, and business reporting.

You will build and optimize pipelines across product catalogs, customer and validation data, applying ETL/ELT design, data modeling, and data quality checks. You will collaborate with ML engineers to prepare clean datasets and productionize ML data flows.

Formación

• Strong programming skills across modern languages and tooling.
• Hands-on experience with pipelines and very large datasets.
• Deep understanding of ETL/ELT design, modeling, partitioning, incremental processing, validation.

Responsabilidades

• Build, maintain, and optimize data pipelines supporting ML workflows and validation processes.
• Work with large-scale structured and semi-structured data across product catalogs, customer, match, and validation data.
• Develop robust ETL/ELT workflows using Python, SQL, Spark or equivalents.
• Support feature generation, data reconciliation, data quality checks, and observability for ML systems.
• Collaborate with ML Engineers to prepare clean datasets for model training and evaluation.
• Improve pipeline performance, scalability, reliability, and cost efficiency.
• Create reusable data processing components and automation utilities.
• Debug data discrepancies across upstream and downstream systems.
• Support operational reporting, dashboards, metric pipelines, and audit workflows.
• Work with engineering teams to productionize ML data flows and handoffs.

Conocimientos

Programming

Data pipelines

ETL/ELT design

Data modeling

Spark

Airflow

AWS Glue

Databricks

BigQuery

Snowflake

Cloud platforms (AWS/GCP)

ML workflows / MLOps

Debug data quality

Monitoring/data quality frameworks

APIs / data formats

Code quality

Herramientas

Spark

Airflow

AWS Glue

Apache Hudi

Databricks

BigQuery

Snowflake

Redshift

Athena

Descripción del empleo

The role requires someone who can bridge data engineering and ML engineering by ensuring that high-quality, well-structured, scalable data flows are available for model training, inference, evaluation, and business reporting.

Key Responsibilities

• Build, maintain, and optimize data pipelines that support ML/ Model workflows, Decision systems, model inference, and validation processes.
• Work with large-scale structured and semi-structured datasets across product catalogs, competitor data, customer data, match data, and validation data.
• Develop robust ETL / ELT workflows using Python, SQL, Spark or equivalent distributed processing frameworks.
• Support feature generation, data reconciliation, data quality checks, and data observability for ML systems.
• Collaborate with ML Engineers and Data Scientists to prepare clean, reliable datasets for model training, evaluation, and inference.
• Improve pipeline performance, scalability, reliability, and cost efficiency.
• Build reusable data processing components and automation utilities.
• Debug data discrepancies across upstream and downstream systems.
• Support operational reporting, dashboards, metric pipelines, and audit workflows.
• Work closely with engineering teams to productionize ML data flows and ensure smooth handoffs between data systems and ML systems.

Required Skills

• Strong programming skills.
• Hands-on experience with data processing pipelines and large datasets.
• Good understanding of ETL / ELT design, data modeling, partitioning, incremental processing, and data validation.
• Experience with tools or platforms such as Spark, Airflow, AWS Glue, Apache Hudi, Databricks, BigQuery, Snowflake, Redshift, Athena, or similar technologies.
• Ability to debug data quality issues and trace data across systems.
• Experience building monitoring, reconciliation, or data quality frameworks.
• Good understanding of APIs, file formats, and data storage formats
• Strong with cloud platforms, preferably AWS and GCP.
• Basic understanding of ML workflows such as training data preparation, inference data generation, evaluation datasets, and feature engineering.
• Experience working with ML pipelines or MLOps workflows.
• Ability to write clean, modular, maintainable, and well-documented code.

Good to Have

• Exposure to vector databases, embeddings, search systems, or retrieval-based systems.
• Experience with product catalog data, e-commerce data, taxonomy, attributes, or entity matching.
• Understanding of model evaluation datasets and metric generation.