Data Engineer

Data Engineer – Full Stack – Python, AI/ML

Location: Remote – US

Duration: 6 Months (possibility of renewal or contract-to-hire thereafter)

Rate: $50–60/hour w2

Join a remote, US-based opportunity supporting a data governance-focused engineering team as a hands-on Data Engineer working across full-stack data engineering and AI/ML-enabled workflows. This role is ideal for a mid-level engineer with experience in Python, SQL, Spark/PySpark, and Airflow, contributing to initiatives involving data quality, lineage, metadata, master data management, and analytics-ready datasets. This is a contract opportunity with the potential for renewal or conversion to a full-time position.

This opportunity offers the chance to work at the intersection of modern data engineering and emerging AI/ML-powered governance practices. You will contribute to initiatives such as embedding-based data classification, anomaly detection, LLM-assisted catalog search, and governed data exposure for AI assistants while partnering with technical teams and stakeholders in a collaborative environment. If you enjoy solving complex data problems, building scalable pipelines, and expanding your expertise in data governance and AI, this role provides strong technical growth potential.

Contract Duration: 6 Months (possible extensions)

Required Skills & Experience

· Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, or related field

· 2+ years of experience building data pipelines using Python (Pandas, NumPy, SciPy) and SQL

· Experience with Apache Spark or PySpark and workflow orchestration tools such as Apache Airflow

· Experience designing schemas across relational and analytical databases including PostgreSQL, MySQL, and SQL Server

· Experience implementing data quality validation, exploratory data analysis (EDA), and integrity enforcement in production datasets

· Hands-on experience with at least one cloud platform (AWS, Azure, or GCP)

· Working familiarity with Python ML libraries such as Scikit-Learn for feature engineering and exploratory analysis

· Experience producing analytics-ready datasets for BI platforms including Tableau, Power BI, or Looker

· Experience with Git, code reviews, CI/CD practices, and modular engineering workflows

· Strong written and verbal communication skills with collaborative working style

Desired Skills & Experience

· Exposure to data governance tooling including metadata management, data lineage, stewardship workflows, and catalogs

· Experience with MDM platforms, especially Informatica MDM SaaS, C360, or multi-domain environments

· Experience supporting compliance, audit, or regulated-data initiatives

· Experience with Apache Kafka and Spark Structured Streaming

· Exposure to lakehouse technologies including Delta Lake and Databricks

· Familiarity with LLM APIs, RAG architectures, agentic AI patterns, and MCP applied to governance use cases

· NLP and text preprocessing experience for unstructured data

· Power BI certifications

· Attention to detail and ownership of data quality outcomes

· Collaborative, team-first mindset with the ability to operate within established engineering standards

· Clear written and verbal communication skills with technical and non-technical audiences

· Curiosity and willingness to grow within modern AI/ML-assisted governance environments

What You Will Be Doing

Tech Breakdown

· 40% Python, SQL, and Data Pipeline Engineering

· 20% Spark/PySpark, Airflow, and Workflow Automation

· 20% Data Governance, Quality, Metadata, and Lineage

· 10% AI/ML-Assisted Governance and Analytics

· 10% BI Reporting and Cloud-Based Data Platforms

Daily Responsibilities

· 80% Hands On

· 0% Management Duties

· 20% Team Collaboration