About Air Space Intelligence ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible. What You Will Do: You will own the reality layer that powers ASI's defense products. You will operate, improve, and expand the data flows that move operational data from mission systems into usable, trusted datasets for decision-making. This role is equal parts data engineering, data forensics, and technical communication. You will dig into real-world data, determine what it actually means, and translate findings into clear, actionable pipelines ready to drive mission-critical decisions for end users. What We Value: - Strong fluency with modern data engineering tooling and patterns: streaming and batch pipelines, schema evolution, data contracts, and lineage. - Demonstrated ability to debug data: profiling, anomaly detection, reconciling sources, and separating signal from noise while cross-validating. - Strong technical communication skills: you can explain what the data is doing, what it means, what is broken, why it matters, and what engineering should change. - Comfort with distributed messaging and processing (Kafka, Flink, Spark, or equivalents) and modern orchestration (Airflow, Dagster, Temporal, or similar). - Strong grasp of API design and integration patterns (REST, gRPC, GraphQL) and experience working across a range of data formats and wire-level protocols (JSON, XML, Protobuf, and binary protocols like JREAP-C or CMF-B). - Working knowledge of modern network protocols, firewalls, system level connections, cross-system authentication, and an enthusiasm to get data flowing. - Familiarity with defense operational data and mission systems, with an appreciation for delayed reporting, inconsistent identifiers, changing semantics, and obscure edge cases. - Comfort operating in classified network environments (e.g., SIPR, JWICS) and working within accreditation boundaries (IL5/IL6, ATO processes). - Experience deploying data infrastructure on Kubernetes and across hybrid cloud and on-prem environments. - A bias for action and distinct aptitude for problem solving in ambiguous environments. - Active SECRET or TOP SECRET U.S. Security Clearance. How We Hire: We look at the interview process not as a screening or test, but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.

• Use of Claude Code to accelerate engineering, orchestration of agentic workflows, and integration via MCP • Develop and optimize scalable, reliable, and efficient data pipelines • Implement processes and tools to ensure data quality, accuracy, and consistency, as well as documentation throughout the data lifecycle • Work with large volumes of heterogeneous data, transforming them into insights to support decision-making • Identify opportunities to optimize workflows and recommend improvements to the existing data infrastructure • Build and maintain analytical models (Gold layer) that support dashboards, reports, and data products for business teams

• Build and manage reliable data pipelines encompassing ingestion/collection, processing, integration, storage, and making data available across the organization. • Work within a distributed systems architecture for massively parallel (MPP) data processing, combining diverse heterogeneous data sources and collaborating with analytics and data science teams to develop solutions and deliver data-driven value.

Role Description Looking for Philippines-based candidates. Job Role: Lead Data Engineer Compensation range: $4,000-5,000 AUD / Monthly (depending on experience and client discretion) Engagement type: Independent Contractor Agreement Work Schedule: This role is expected to align with the AU business hours (approx. 9 AM - 5 PM, Monday to Friday) for collaboration, but as a contractor, you’ll have flexibility in how you manage your time. Role Overview: This role is central to the successful design, build, and ongoing governance of a DataHub. The Lead Data Engineer acts as the primary interface between offshore and onshore teams, setting architectural direction and engineering standards while ensuring consistent code quality and adherence to agreed design principles. During the build phase, the role oversees architecture decisions, coordinates with IT teams on core data layer delivery, and approves work prior to UAT release. As delivery transitions from build to run, the role shifts into operational ownership—managing DataHub SLAs, overseeing new dataset intake, and serving as the escalation point for production issues. The role also requires strong stakeholder engagement, including facilitating agile ceremonies and communicating effectively with both technical and non-technical audiences across a distributed, cross-timezone team. Key Responsibilities - Own the dbt project architecture, including model structure, naming conventions, testing standards, CI/CD pipelines, and documentation frameworks to ensure consistency across all engineering work. - Manage the dataset delivery backlog with onshore stakeholders, including phase scoping, validation of source data availability, and sequencing of builds to maximise business value. - Govern code quality by reviewing all Silver and Gold models prior to UAT, ensuring no production deployment occurs without lead approval. - Own the end-to-end dataset handover process from Build to Run, including test sign-off, data catalogue documentation, observability configuration, and operational runbooks. - Coordinate and prioritise work across Build and Run workstreams operating in parallel, ensuring alignment and delivery efficiency. - Participate in standups (async where required) and lead sprint demonstrations on a fortnightly basis. - In Run steady state operations, own DataHub SLA performance, oversee new dataset onboarding, and act as the final escalation point for production support issues. Run & Steady-State Responsibilities - Own DataHub SLA performance, ensuring pipeline uptime and data freshness commitments are met across all production Gold datasets. - Manage new dataset intake from onshore teams, scoping requests and determining whether they require a new build phase or can be absorbed into the Run team. - Act as the primary escalation point for onshore stakeholders and the final offshore escalation for critical incidents that cannot be resolved by the data operations team. Qualifications - 7+ years data engineering experience (including 2+ years in a team lead or principal engineer role) - Snowflake (advanced): warehouse management, RBAC, dynamic data masking, Snowflake Tasks, performance tuning, and query optimisation - dbt Cloud (advanced): incremental models, snapshots, macros, packages, Semantic Layer / MetricFlow, CI/CD using GitHub Actions - Python (intermediate): Snowpark, pipeline scripting, and data quality automation - Data modelling (expert): dimensional modelling, medallion architecture (Bronze / Silver / Gold), and retail data models (orders, inventory, customers) - Git (advanced): branching strategies, PR workflows, and managing shared dbt projects across multiple engineers - Azure Data Factory (intermediate): pipeline monitoring and troubleshooting; ability to engage IT credibly on pipeline issues (build owned by IT) - Retail domain knowledge (preferred): familiarity with order management, inventory planning, and DTC data structures - AI & Automation Tools: Hands-on experience with tools such as GitHub Copilot, Cursor, Claude (or similar LLMs), Snowflake Cortex AI_COMPLETE, Elementary, and data observability platforms for AI-assisted development, SQL debugging, anomaly detection, alert classification, incident reporting, and workflow automation. What Success Looks Like - Excited to build and scale a modern data platform from the ground up - Proven experience leading data engineering teams and maintaining engineering quality standards - Comfortable working with separate upstream data delivery teams - Has built dbt medallion architecture projects (Bronze / Silver / Gold) from scratch - Strong quality ownership; confident holding back datasets not ready for UAT - Uses AI tooling naturally as part of day-to-day engineering workflows - Owns AI triage pipeline accuracy and incident log quality - Ensures alerts are acknowledged and resolved within agreed SLAs - Maintains accurate documentation and quality certifications across Gold datasets - Experienced in operating and tuning data pipeline monitoring and alerting processes - Able to communicate incidents clearly to business stakeholders in concise, non-technical language - Thrives in stable, operational environments where reliability is critical - Understands escalation boundaries and collaborates effectively with senior engineers Work Arrangement & Expectations This is a remote role that will be set up as an independent contractor engagement. To ensure alignment and transparency, successful candidates will be expected to: - Disclose any existing ongoing roles or client work - Reflect this engagement on their LinkedIn profile (clearly marked as “Independent Contractor”)