Senior Data Engineer

Overview

We are looking for Senior Data Engineer to work with our team and our clients to develop enterprise grade data platforms, services, pipelines, data models, visualizations, and more! The engineer needs to be a technologist with excellent communication and customer service skills and a passion for data and problem solving. This role involves technical and thought leadership across the spectrum of data capabilities, from data vision and strategy all the way through data science.

At Steampunk, our goal is to build and execute a data strategy for our clients to coordinate data collection and generation, to align the organization and its data assets in support of the mission, and ultimately to realize mission goals with the strongest effectiveness possible.

For our clients, data is a strategic asset. They are looking to become a facts-based, data-driven, customer-focused organization.  To help realize this goal, they are leveraging visual analytics platforms to analyze, visualize, and share information.  At Steampunk you will design and develop solutions to high-impact, complex data problems, working with the best and data practitioners around. Our data exploitation approach is tightly integrated with Human-Centered Design and DevSecOps.

Contributions

• Designing and leading the implementation of greenfield data solution stacks in the cloud or on premises, using the latest data services, products, technology, and industry best practices
• Expand upon existing models to estimate how long it will take clients to process digital forms/applications and to present relevant information to users about their application processing journey
• Develop and deploy personalized Application Processing Time predictive models for additional services/forms as prioritized by client
• Add integration points for intelligent automated help agent to connect with external applications and/or data sources, to increase the agent’s effectiveness and accuracy
• Create and implement data cleaning and processing strategies to ensure unstructured data is appropriate to present to the end user/customer
• Analyze customer data and biometrics to identify key data points for confirming a customer’s identity
• Design and implement data acquisition pipelines to ensure clean structured, and high-quality data ingestion from a web application with form-based inputs, applying validation, transformation, and deduplication techniques to enhance data integrity
• Develop scalable data integration solutions by enabling real-time data sharing across enterprise systems using technologies (e.g., Kafka, AWS Kinesis, oar Apache Pulsar), ensuring seamless interoperability and adherence to data governance standards
• Data Architecture contributions include assessing, understanding, and implementing data sources, data models and schemas, and data workflows
• Data Engineering contributions include assessing, understanding, designing, and implementing ETL jobs, data pipelines, and workflows
• Analyze, validate, and create an ETL process to transition from a transaction-based model to a customer-centric model
• Data Science contributions include assessing, understanding, designing, and implementing machine learning and AI applications, designing MLOps pipelines, and supporting data scientists
• Addressing technical inquiries concerning customization, integration, enterprise architecture and general feature / functionality of data products
• Enhance chatbot intelligence and user experience by refining LLM and natural language processing (NLP) models, improving response accuracy, and optimizing conversational flows using machine learning techniques and user interaction data
• Support refactoring and optimizing chatbot architecture to improve scalability, efficiency, and maintainability , leveraging advanced AI/ML techniques, integrating with modern data pipelines, and implementing robust monitoring and analytics for continuous improvement
• Key must have skill sets – broad understanding of data exploitation lifecycle and capabilities, technical leadership in the data field
• Support an Agile software development lifecycle

Qualifications

Qualifications:

• Ability to hold a position of public trust with the US government.
• 5+ years industry experience leading the design and implementation of data systems
• 10+ years direct experience in Data Solutions with experience in tools such as:
  • Big data tools: Spark
  • Relational databases: Amazon RDS (PostgreSQL)
  • Cloud Services: AWS services including RDS, ECS, EKS, ElastiCache, SES
  • Data pipeline and workflow management tools: Jenkins (for automation and CI/CD i