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