Role Description This isn't a role where you'll be handed a ticket queue. You'll own meaningful pieces of work, shape how we build, and have a direct hand in what the company becomes over the next few years. Responsibilities - Design and deliver modern data platforms using tools like Snowflake, Microsoft Fabric, Azure, Fivetran, Coalesce, dbt, and Power BI. - Lead projects end-to-end - from architecture through to client handover. - Champion data governance practices that make platforms trustworthy and scalable. - Support clients through the change that comes with adopting new ways of working with data. - Help build internal standards and frameworks that shape how the whole team works. - Lift capability across the team - by doing good work and being someone others learn from. Qualifications - 5+ years of experience in data engineering or modern data platform delivery. - Strong SQL and Python, plus hands-on experience with Snowflake, Microsoft Fabric, Fivetran, Coalesce, or dbt. - Solid understanding of medallion architecture and dimensional modelling. - Familiarity with data governance and metadata tools like Microsoft Purview and Coalesce Catalog. - Comfortable using AI tools to accelerate development, whether that's Claude, Snowflake Cortex, or similar. - Experience building data pipelines and data products that support AI and ML use cases, including an understanding of what clean, well-structured data looks like for model consumption. - A track record of owning projects and delivering results, not just contributing to them. - The communication skills to work directly with clients and earn their trust. - Previous consulting experience is a plus, but a client-first mindset matters more.

• Design the end-to-end System Architecture on a modern cloud/data platform (e.g., Azure / Microsoft Fabric) • Define the Knowledge Graph ontology (entities, relationships, business rules), using platforms such as Microsoft Fabric IQ or equivalent • Map source-to-graph integrations into the target data lake/lakehouse (e.g., OneLake) • Design the LLM enrichment strategy (taxonomy, prompts, evaluation) using Azure OpenAI or equivalent LLM services • Produce the phased data roadmap for the Build phase • Align technical direction with client stakeholders

Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand. Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence. About the team:With our success over the past 10 years, we are ready to move beyond our current technologies and build the next generation ML Platform to align with our current business needs as we continue to grow. Currently using a custom setup, we handle 1.5 billion ML inferences per second, every day. We are now building a new ML platform to support neural networks at that scale to increase the intelligence of our models. As a Software Engineer on the Machine Learning Data Platform team at Liftoff, you will: - Work with an experienced team of ML, Software, and Infrastructure Engineers that are building the ML platform for Liftoff - Design, engineer & implement reliable, scalable, and cost-efficient systems to support data lake infrastructure, dataset generation, model training infrastructure, analytics, reporting and monitoring infrastructure - Deliver business results by enabling next generation ML models and technologies - Utilize vendor-based products (AWS, Weight & Biases, etc.), open-source technologies (PyTorch, PySpark, Trino, Hive, Iceberg, Clickhouse, etc.) and in-house tooling Desired qualities and experience: - Very strong coding ability in Python and Golang. - Strong core CS fundamentals (data structures, algorithms, architecting systems) - 6+ years of industry experience - Experience with Spark is a plus. - Experience working in a high-growth startup atmosphere is a plus - Experience building Machine Learning tooling or platforms, in particular when applied to large-scale problems, is a big plus - A passion for quality and excellence, and the ability to temper it when necessary to ship Location: This role is eligible for hybrid work, 2 days per week in one of our Bay Area offices in Redwood City or San Francisco. Travel Expectations: We offer several opportunities for in-person team gatherings, including but not limited to project meetings, regional meetups, and company-wide events. We expect our employees to attend these gatherings at least once per quarter. These gatherings provide essential opportunities for collaboration, communication, and team building. Compensation: Liftoff offers all employees a full compensation package that includes equity and health/vision/dental benefits associated with your country of residence. Base compensation will vary based on the candidate's location and experience. The following are our base salary ranges for this role: - SF Bay Area: $180,000 - $230,000 #LI-CM1 Liftoff offers a fast-paced, collaborative, and innovative work environment where employees are empowered to grow and make an impact. We’re shaping the future of the mobile app ecosystem—join us and help accelerate what’s next. Liftoff’s compensation strategy includes competitive salaries, equity, and benefits designed to support employee well-being and performance. We benchmark compensation based on role, level, and location to ensure fairness and market alignment. Benefits may include medical coverage, wellness stipends, and additional perks based on your country of residence. Liftoff is an equal opportunity employer. We are committed to creating an inclusive environment for all employees and applicants regardless of race, ethnicity, national origin, age, marital status, disability, sexual orientation, gender identity, religion, veteran status, or any other characteristic protected by applicable law. Agency and Third Party Recruiter Notice: Liftoff does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or Recruiting Team. All candidates must be submitted via our Applicant Tracking System by approved Liftoff vendors who have been expressly requested to make a submission by our Recruiting Team for a specific job opening. No placement fees will be paid to any firm unless such a request has been made by the Liftoff Recruiting Team and such a candidate was submitted to the Liftoff Recruiting Team via our Applicant Tracking System.

• Designs, builds, and maintains resilient ETL/ELT pipelines that ingest data from on-premise systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), centralizing and curating data for consumption in Snowflake and downstream AI services. • Develops and maintains feature stores and analytically optimized datasets that support machine learning workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams. • Engineers data pipelines that enable generative AI use cases, including the automated extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments. • Acts as a Snowflake power user and technical lead, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high-concurrency analytics and AI workloads. • Executes non-invasive data extraction strategies to unlock mission-critical data from decades-old legacy systems while preserving system stability and avoiding disruption to core business operations. • Designs and manages complex, cross-platform data workflows using orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory to ensure reliable, synchronized data movement across the organization’s multi-cloud architecture. • Partners closely with central IT, database administrators, infrastructure, and security teams to resolve connectivity and access challenges—including PrivateLink, IAM, network segmentation, and firewall controls—while securing production approval for new data integrations. • Implements automated data quality, validation, and observability frameworks to detect data drift, anomalies, and integrity issues that could negatively impact production analytics, machine learning, or AI systems. • Drives efficiency across the data ecosystem by optimizing storage, compute usage, and query performance in Snowflake, AWS, and Azure, ensuring responsible cost management and measurable ROI for Transformation Office initiatives. • Operates as a dedicated engineering partner to MLOps, Data Science, and AI teams, rapidly iterating on evolving data requirements and translating experimental use cases into scalable, production-ready data solutions.