Hybrid Data Engineer: Stats & Ml Federal Data

Lead Data Engineer

RESP & QUALIFICATIONS

PURPOSE:

The Lead Data Engineer is responsible for orchestrating, deploying, maintaining and scaling Cloud OR on-premise infrastructure targeting big data and platform data management (Relational and No

SQL, distributed and converged) with emphasis on reliability, automation and performance. This role will focus on leading the development of solutions and helping transform the company's platforms deliver data‑driven, meaningful insights and value to company.

• Lead the team to design, configure, implement, monitor, and manage all aspects of Data Integration Framework. Defines and develop the Data Integration best practices for the data management environment of optimal performance and reliability.
• Develops and maintains infrastructure systems (e.g., data warehouses, data lakes) including data access APIs. Prepares and manipulates data using Hadoop or equivalent Map Reduce platform.
• Provides detailed guidance and performs work related to Modeling Data Warehouse solutions in the Cloud OR on-premise. Understands Dimensional Modeling, De‑normalized Data Structures, OLAP, and Data Warehousing concepts.
• Oversees the delivery of engineering data initiatives and projects. Supports long‑term data initiatives as well as Ad‑Hoc analysis and ELT/ETL activities. Creates data collection frameworks for structured and unstructured data. Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources.
• Enforces the implementation of best practices for data auditing, scalability, reliability and application performance. Develop and apply data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources.
• Interprets data, analyzes results using statistical techniques, and provides ongoing reports. Executes quantitative analyses that translate data into actionable insights. Provides analytical and data‑driven decision‑making support for key projects. Designs, manages, and conducts quality control procedures for data sets using data from multiple systems.
• Improves data delivery engineering job knowledge by attending educational workshops; reviewing professional publications; establishing personal networks; benchmarking state‑of‑the‑art practices; participating in professional societies.

Position does not have direct reports but is expected to assist in guiding and mentoring less experienced staff. May lead a team of matrixed resources.

• Education Level: Bachelor's Degree in Computer Science, Information Technology or Engineering or related field OR in lieu of a bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience.
• Experience:
  
  8 years' Experience in leading data engineering and cross functional team to implement scalable and fine‑tuned ETL/ELT solutions for optimal performance. Experience developing and updating ETL/ELT scripts. Hands‑on experience with application development, relational database layout, development, data modeling.
• Preferred Qualifications:
  
  Proven hands‑on experienced AWS Redshift Administrator to engineer, operate, secure, and optimize our cloud data warehouse platform. AWS Certified Data Analytics Specialty. AWS Certified Solutions Architect. Experience in regulated or enterprise environments. Experience with cloud migrations. Experience with data lake architectures. Hands‑on experience administering Redshift at enterprise scale (RA3/Serverless/Spectrum), including scaling, patching, backups/snapshots, and disaster recovery. Proven Redshift performance tuning (WLM/concurrency scaling, sort/dist keys, compression/encoding) with a focus on cost optimization.
  
  Strong security and governance background (IAM, KMS/TLS encryption, row/column‑level controls, audit logging, compliance). Experience integrating Redshift with S3/Glue/Lake Formation and supporting ETL/ELT pipelines, Spectrum, and external schemas. Automation/Dev Ops mindset:
  Infrastructure as Code (Terraform/Cloud Formation) plus CI/CD and blue/green deployment practices.…