Job Description At AstroPay, we are transforming how decisions are made through data. As Head of Data Engineering, you will own the data engineering vision, architecture, and execution strategy, ensuring our data platform is scalable, reliable, cost‑efficient, and aligned with business growth. This role goes beyond hands‑on development: you’ll define the data engineering roadmap, lead and scale a high‑performing team, and act as the bridge between business strategy, analytics, and engineering excellence. Your mission is to ensure that our data infrastructure consistently delivers trusted, high‑impact data products across the organization. Job Requirements Must‑Have 8 years of experience in Data Engineering, with proven ownership of large‑scale data platforms. Strong background designing and maintaining ETL pipelines and Data Lake architectures. Advanced SQL expertise, particularly with BigQuery or similar big data technologies. Strong proficiency in Python for data pipelines and automation. Hands‑on experience with relational and non‑relational databases. Proven experience leading and mentoring engineering teams. Strong communication skills, with the ability to translate technical concepts to business stakeholders. Nice to Have Experience working in high‑growth environments or startups. A strong product mindset, understanding the “why” behind every data pipeline. Experience enabling analytics, BI, and experimentation use cases. Passion for continuous improvement and technical curiosity. Job Responsibilities Data Engineering Strategy & Architecture Own the end‑to‑end data engineering strategy, aligning infrastructure decisions with business and product priorities. Define and evolve the Data Lake / Data Platform architecture, ensuring scalability, reliability, and performance. Evaluate, challenge, and improve existing data architectures, participating in high‑level technical and strategic decisions. Translate complex business needs into robust, long‑term technical solutions. Technical Leadership & Execution Lead the execution of the data product and platform roadmap, coordinating priorities across teams. Set engineering standards for ETL pipelines, data modeling, automation, and orchestration. Oversee advanced SQL usage in BigQuery, ensuring efficient processing of large‑scale datasets. Drive best practices in Python‑based data pipelines, automation, CI/CD, and GitHub workflows. Team Leadership & Mentorship Build, mentor, and scale a high‑performing team of Data Engineers. Act as a technical reference point, fostering a culture of engineering excellence, ownership, and continuous improvement. Promote knowledge sharing, documentation, and sustainable development practices across the team. Quality, Reliability & Efficiency Ensure data quality, availability, and consistency across the platform. Own SLAs, incident management, performance monitoring, and cost optimization. Define and track engineering KPIs to guarantee reliability and efficiency at scale. Cross‑Functional Collaboration Partner closely with Data Analytics, Product, Growth, and Engineering to enable trusted, analytics‑ready data. Ensure data infrastructure supports experimentation, reporting, and business intelligence needs. Serve as a key stakeholder in company‑wide discussions around data, scalability, and platform evolution. J-18808-Ljbffr