MLOps & Data Engineer

Are you looking to leverage your technical creativity? Dream, Innovate, Inspire and Empower the next generation to transform humanity through technology and imagination! As a Data Engineer II, you will oversee the development and optimization of complex data pipelines and transformations. You will collaborate with cross-functional teams to ensure the efficient and secure integration, management, and utilization of data within the Enterprise Data Warehouse (EDW). Your role will also involve mentoring junior data engineers and implementing best practices.As SNC's corporate team, we provide the company and its business areas with strategic direction and business support spanning executive management, finance and accounting, operations, human resources, legal, IT, information security, facilities, marketing, and communications. Responsibilities : Oversee the design and development of data pipelines and transformations that feed AI and ML systems across the platform Collaborate with the AI/LLM Platform team and other engineering teams to understand data and model requirements and ensure effective solutions. Mentor and guide junior data engineers. Develop and enforce best practices for data engineering processes. Implement advanced data integration, data management, and data quality solutions, including experiment tracking and model registry systems Perform testing, debugging, and optimization of data pipelines and model serving infrastructure. Ensure data security and compliance with CMMC and SNC data governance standards. Develop and maintain comprehensive documentation for data processes. Design and operate feature stores and model serving infrastructure supporting real-time and batch inference Manage graph and relational data stores supporting AI applications such as knowledge graphs and entity resolution Monitor data pipeline and model serving health, participating in on-call rotation for data infrastructure. Qualifications You Must Have: Bachelor's degree in Computer Science, Data Engineering, or a related field. 2+ years of experience in data engineering or a related role. Higher level relevant degree may substitute for experience. Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 6 years of related experience is required. Proficiency in SQL and experience with ETL/orchestration tools such as Airflow, dbt, or Prefect. Experience with cloud platforms such as AWS, Azure, or Google Cloud. Strong problem-solving and analytical skills. Working SQL knowledge and experience working with relational databases. Experience with AWS cloud services: S3, Redshift, Glue. Experience building data pipelines, architectures, and data sets. Experience performing root cause analysis on data to answer specific business questions or issues. Strong Python skills, with experience in data engineering frameworks such as Spark, dbt, Airflow, or Prefect. Operational responsibilities (schedules, monitoring, logging, alerting, error handling, etc.) Exposure to ML lifecycle tools such as MLflow, Weights & Biases, or Kubeflow. Qualifications We Prefer: Experience building data pipelines, architectures, and data sets. Experience performing root cause analysis on data to answer specific business questions or issues. Experience with AWS cloud services (e.g., S3, EC2, RDS, Redshift, Glue, Lambda, Step Functions, Athena, CloudWatch, ECS, IAM). Experience with object-oriented scripting languages and frameworks (e.g., Python, Java). Familiarity with source system integration patterns (e.g., SQL, APIs). Exposure to operational responsibilities (schedules, monitoring, logging, alerting, error handling, etc.). Basic understanding of Master Data Management (MDM) concepts and exposure to MDM solutions. Familiarity with DevOps practices and tools, with some hands-on experience in a CI/CD environment. Experience with big data technologies (e.g., Hadoop, Spark). Certifications in data engineering or related fields. Essential Functions: Ability to work on a computer for extended periods. Frequent communication with team members and stakeholders. Ability to work in an office or hybrid environment. Occasional travel may be required. Must be able to lift up to 10 lbs occasionally. Ability to ensure data engineering practices adhere to industry-specific regulations and security standards (e.g., ITAR, DFARS, NIST), safeguarding sensitive data throughout the data lifecycle. Experience with ML lifecycle tools such as MLflow, Weights & Biases, or Kubeflow Familiarity with feature store platforms (Feast, Tecton, Hopsworks) Experience with graph databases (Neo4j, Amazon Neptune) supporting knowledge graphs or entity resolution Familiarity with vector databases or retrieval-augmented generation (RAG) pipelines (Pinecone, Weaviate, pgvector) Experience with Kubernetes for deploying data or ML workloads Familiarity with data observability tools (Monte Carlo, Great Expectat #J-18808-Ljbffr snc