Data Engineer – AI & Analytics

Computer Task Group, Inc•UNAVAILABLE, UNAVAILABLE

13h•Remote

About The Position

CTG is seeking to fill a Data Engineer – AI & Analytics position for our client. Join a high-impact team helping organizations modernize their data ecosystems to power next-generation AI, machine learning, and analytics solutions. This role is ideal for an experienced Data Engineer who thrives on designing scalable data platforms, building modern data pipelines, and transforming complex data into trusted, AI-ready assets.

Requirements

Advanced proficiency in Python and SQL for enterprise data engineering.
Experience with PySpark or comparable distributed processing frameworks.
Hands-on experience with Databricks, Apache Spark, or similar cloud data platforms.
Experience using Apache Airflow or equivalent workflow orchestration tools.
Strong knowledge of Parquet, Delta Lake, and modern analytical storage formats.
Experience building streaming solutions using Kafka or equivalent messaging platforms.
Proficiency with Docker, Git, CI/CD pipelines, and DevOps practices.
Strong understanding of cloud data architectures, API development, and distributed systems.
Knowledge of data modeling, performance tuning, and scalable data architecture design.
Familiarity with Master Data Management (MDM), data governance, data lineage, PII compliance, and responsible AI data practices.
Excellent analytical, troubleshooting, and collaboration skills.
5+ years of experience in data engineering, cloud data platforms, or big data development.
Demonstrated success designing enterprise-scale data platforms supporting AI, analytics, or machine learning workloads.
Experience developing robust ETL/ELT pipelines across structured, semi-structured, and streaming data sources.
Strong background in application development, API development, debugging, and performance optimization.
Experience working with modern cloud technologies and distributed computing environments.
Ability to translate complex business and technical requirements into scalable data architecture solutions.

Nice To Haves

Exposure to analytics libraries, statistical computing frameworks, and Natural Language Processing (NLP) technologies is a plus.

Responsibilities

Assess customer data environments to evaluate data quality, structure, lineage, and AI/ML readiness.
Design, build, and optimize scalable ETL/ELT pipelines across APIs, relational databases, cloud storage, files, and streaming platforms.
Develop cloud-native data infrastructure supporting enterprise AI, analytics, and machine learning initiatives.
Build high-performance batch and real-time data processing pipelines.
Create reusable, production-ready data assets for analytics, business intelligence, and machine learning teams.
Implement best practices for data governance, security, privacy, metadata management, and regulatory compliance.
Optimize data models, storage formats, and pipeline performance for large-scale processing.
Develop and maintain technical documentation, architecture diagrams, and operational procedures.
Collaborate with data scientists, software engineers, analysts, and business stakeholders to deliver scalable data solutions.
Troubleshoot and resolve complex data integration and performance challenges across modern and legacy environments.