Data Engineer

• Lead the design and implementation of scalable data pipelines (batch and real-time), ensuring reliability, performance, and data quality across platforms.
• Architect and maintain ETL/ELT workflows with strong observability, lineage tracking, and governance best practices built in from the ground up.
• Design data solutions that support AI and LLM use cases, including unstructured data ingestion, embedding pipelines, and vector-based retrieval systems.
• Own cloud-based data architecture within AWS, leveraging services such as S3, Redshift, Glue, Athena, Kinesis, and Lambda to support analytics and AI workloads.
• Establish data standards, contracts, and best practices across projects while mentoring engineers and ensuring consistency in delivery.
• Partner with cross-functional teams to translate business and product requirements into scalable, maintainable data solutions.
• Lead technical planning for data workstreams, including task breakdown, estimation, and coordination within project delivery timelines.
• Collaborate with stakeholders to assess data readiness, identify gaps, and recommend improvements for analytics and AI adoption.
• Contribute to secure, compliant architecture design, including environments with regulatory or government requirements when applicable.
• Support documentation, architecture design artifacts, and client-facing deliverables to ensure transparency and long-term maintainability.

Our client is seeking a highly experienced Senior Data Engineer who can operate independently while leading the design and delivery of scalable, cloud-based data solutions. This individual will play a critical role in building data foundations that support advanced analytics and AI-driven applications, including LLM-enabled systems. The ideal candidate combines deep technical expertise with strong communication skills, enabling them to partner effectively across engineering teams and client stakeholders., * 5+ years of hands-on data engineering experience, including progression into senior or lead-level responsibilities.

• Strong experience designing and deploying data solutions within AWS environments using core data services and cloud-native architectures.
• Proven ability to build and optimize production-grade data pipelines, including both batch and streaming data processing.
• Experience supporting AI or machine learning workflows, with exposure to LLM-related data structures such as embeddings or vector storage.
• Solid understanding of data modeling, warehousing concepts, and pipeline performance optimization.
• Experience with infrastructure-as-code and automation tools such as Terraform, CDK, or similar frameworks.
• Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.
• Experience working in collaborative, cross-functional environments, ideally within consulting or multi-project delivery settings.
• Familiarity with modern data tools such as Databricks, dbt, Airflow, or similar orchestration frameworks is preferred.
• Bachelor's degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience.