• Collaborate with Data Science, Product Managers and Software Engineers to build robust ETL pipelines that enable the Product Support team to deliver compelling user-facing features • Contribute to architecture decisions, observability tooling, and data quality initiatives that keep our platform robust and maintainable. • Contribute to a scalable internal framework for managing prompt engineering pipelines and other AI workflows. • Enforce and elevate engineering best practices across the AI/ML org, including code quality, testing, and documentation.

Role Description 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. Location: Remote Duration: 5 months Duties: - 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. Qualifications - 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. - Exposure to analytics libraries, statistical computing frameworks, and Natural Language Processing (NLP) technologies is a plus. - Excellent analytical, troubleshooting, and collaboration skills. Requirements - 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. Education - Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical discipline. - Equivalent professional experience will also be considered. - Excellent verbal and written English communication skills and the ability to interact professionally with a diverse group are required. Benefits - The expected base salary for this position ranges from $70.00 to $75.00/hour. - Salary offers are based on a wide range of factors including relevant skills, training, experience, education, market factors, and where applicable, licensure or certifications obtained. - In addition to salary, a competitive benefit package is also offered. To Apply To be considered, please apply directly to this requisition using the link provided. Kindly forward this to any other interested parties. Thank you!

Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference. As a Lead Data Engineer - Python/PySpark/Databricks/AWS/AI at JPMorganChase within the Consumer & Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities - Generates data models for their team using firmwide tooling, linear algebra, statistics, and geometrical algorithms - Delivers data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way - Implements database back-up, recovery, and archiving strategy - Evaluates and reports on access control processes to determine effectiveness of data asset security​ with minimal supervision - Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and model design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements. - Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., backup/recovery validation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations. Required qualifications, capabilities, and skills - Formal training or certification on Data Science engineering concepts and 5+ years applied experience - Expertise with Python, PySpark, Databricks, Snowflake, AWS and AI - Working experience with both relational and NoSQL databases​ - Experience and proficiency across the data lifecycle - Experience with database back-up, recovery, and archiving strategy - Proficient knowledge of linear algebra, statistics, and geometrical algorithms - Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity. - Ability to review and validate AI-assisted outputs (e.g., model/design summaries or operational checklists) before use, escalating when uncertain and following data handling requirements. Preferred qualifications, capabilities, and skills - Exposure to cloud technologies - Hands-on experience on Kafka or any streaming technology - Hands-on experience in Splunk, Dynatrace tools - Exposure to AI Driven development About UsChase is a leading financial services firm, helping nearly half of America’s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. Equal Opportunity Employer/Disability/Veterans About the TeamOur Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.

• Define and drive the data science and AI roadmap, aligning model development priorities with business objectives and product strategy. • Lead end-to-end delivery of ML and AI solutions — from problem framing, data discovery, and model design through validation, deployment, and performance monitoring. • Translate ambiguous business problems into well-scoped data science workstreams, identifying quick wins alongside longer-term strategic initiatives. • Champion best practices in model development, including versioning, documentation, validation, and observability. • Design and implement NLP pipelines for use cases such as entity extraction, semantic mapping, classification, and retrieval-augmented generation (RAG). • Build and maintain forecasting and predictive models to support operational and strategic decision-making. • Apply statistical and machine learning methods to identify root causes of process inefficiencies and data quality issues. • Develop reusable data pipelines, crosswalk tables, and transformation workflows that support scalable, cross-functional data products. • Conduct current-state assessments of data architecture, sources, and quality; define future-state data models and governance standards. • Develop and maintain KPI reporting frameworks and dashboards that enable performance monitoring and data-driven decision-making. • Apply process optimization methodologies (e.g., Lean Six Sigma) to identify bottlenecks, reduce cycle time, and improve data accuracy. • Ensure analytical outputs are accurate, auditable, and aligned with regulatory and compliance requirements (e.g., HIPAA, GDPR). • Partner closely with Product, Engineering, and business stakeholders to clarify requirements, validate feasibility, and define measurable success criteria. • Communicate complex analytical findings and model outputs clearly to both technical and non-technical audiences, including executive stakeholders. • Define value-realization strategies for data and AI investments, ensuring ROI is tracked through improved search, reporting, and operational insight. • Mentor data analysts and junior data scientists through pairing, design reviews, and structured technical guidance. • Lead knowledge transfer of owned models, pipelines, and analytical frameworks to ensure team resilience and continuity. • Drive a culture of continuous learning, analytical rigor, and responsible AI within the data science function.