Data Engineering Team Lead - Applied AI Engineering Group

At Dream, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; it’s a Dream job. Dream is where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Let’s build something extraordinary together.

Dream's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Central to our Dream's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.

At Dream, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.

The Dream Job:

It starts with you - a technical leader who’s passionate about data pipelines, data modeling, and growing high-performing teams. You care about data quality, business logic correctness, and delivering trusted data products to analysts, data scientists, and AI systems. You’ll lead the Data Engineering team in building ETL/ELT pipelines, dimensional models, and quality frameworks that turn raw data into actionable intelligence.

If you want to lead a team that delivers the data products powering mission-critical AI systems, join Dream’s mission - this role is for you.

The Dream-Maker Responsibilities:

• Lead and grow the Data Engineering team - hiring, mentoring, and developing engineers while fostering a culture of ownership and data quality.
• Define the data modeling strategy - dimensional models, data marts, and semantic layers that serve analytics, reporting, and ML use cases.
• Own ETL/ELT pipeline development using platform tooling - orchestrated workflows that extract from sources, apply business logic, and load into analytical stores.
• Drive data quality as a first-class concern - validation frameworks, testing, anomaly detection, and SLAs for data freshness and accuracy.
• Establish lineage and documentation practices - ensuring consumers understand data origins, transformations, and trustworthiness.
• Partner with stakeholders to understand data requirements and translate them into well-designed data products.
• Build and maintain data contracts with consumers - clear interfaces, versioning, and change management.
• Collaborate with Data Platform to define requirements for new platform capabilities; work with Datastores on database needs; partner with ML, Data Science, Analytics, Engineering, and Product teams to deliver trusted data.
• Design retrieval-friendly data products - RAG-ready paths, feature tables, and embedding pipelines - while maintaining freshness and governance SLAs.
  
  

• 8+ years in data engineering, analytics engineering, or BI development, with 2+ years leading teams or technical functions. Hands-on experience building data pipelines and models at scale.
• Data modeling - Dimensional modeling (Kimball), data vault, or similar; fact/dimension design, slowly changing dimensions, semantic layers
• Transformation frameworks - dbt, Spark SQL, or similar; modular SQL, testing, documentation-as-code
• Orchestration - Airflow, Dagster, or similar; DAG design, dependency management, scheduling, failure handling, backfills
• Data quality - Great Expectations, dbt tests, Soda, or similar; validation rules, anomaly detection, freshness monitoring
• Batch processing - Spark, SQL engines; large-scale transformations, optimization, partitioning strategies
• Lineage & cataloging - DataHub, OpenMetadata, Atlan, or similar; metadata management, impact analysis, documentation
• Messaging & CDC - Kafka, Debezium; event-driven ingestion, change data capture patterns
• Languages - SQL (advanced), Python; testing practices, code quality, version control