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Data Engineering with Machine Learning

Modern Agile Technologies, LLCCorpus Christi, TX🇺🇸United StatesPosted 14 Sept 2026

Why This Role Stands Out

This role offers a fantastic opportunity to build and scale data pipelines and productionize ML models within a reputable technology company. You'll thrive here if you have a strong foundation in advanced statistics, ML, and data engineering, coupled with a passion for MLOps. Apply today to contribute to cutting-edge data solutions!

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
Corpus Christi, TX, United States
Posted
2 days ago
DockerSQLETLMLOpsMLflowMachine LearningSnowflakeAgileAirflowBigQueryKubernetesPythonRedshift

Job Description

Hiring,

Greetings from  Modern Agile Technologies.

Position: Sr. Data Engineering + Machine Learning + MLOps 

Location: Corpus Christi, TX – onsite.

Type:        Contract (W2 Only)

 

Required skills:
Advanced statistics and ML modeling (hypothesis testing, experimentation, feature engineering, evaluation, calibration). Strong SQL/Python for large-scale data wrangling (joins, windows, tuning, cleansing, reconciliation, automated quality checks). Data engineering for scalable batch/streaming pipelines (ETL/ELT, CDC, incremental, Airflow/Prefect/Dagster). Deep knowledge of modern data platforms (lakehouse/warehouse, S3/ADLS, Snowflake/BigQuery/Redshift, Parquet/Delta/Iceberg, partitioning, access control). MLOps deployment with Docker, Kubernetes, CI/CD, MLflow, and end‑to‑end monitoring.

Nice to have skills:
"Build and maintain production-grade data pipelines (batch and streaming) with clear SLAs, retries, idempotency, and automated backfills.

Integrate and model data across sources by defining schemas, keys, transformations, and curated layers that support analytics and ML consumption.

Implement data quality, observability, and governance controls, including validation rules, lineage, access controls, and anomaly detection on data freshness and volume.

Productionize analytics and ML by packaging models, deploying services or jobs, managing versioning, and monitoring drift, performance, and latency.

Serve trusted data products to consumers via optimized warehouse tables, feature stores, and APIs, ensuring secure access and predictable query performance."

 

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