About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information. Our EDP team builds core data backend capabilities that power critical product experiences across ingestion, profile processing, and serving.

About the role

We are hiring a Senior Software Engineer, Data Backend to design and operate production-grade data backend systems at scale. This role combines backend service engineering and data platform engineering, with strong focus on reliability, performance, and cost efficiency in a cloud-native environment.

You will work on high-throughput APIs/services in Go, as well as data processing and workflow components in Python/Spark/SQL, partnering closely with product, platform, and data teams.

What you will do

• Design, build, and maintain backend services and data platform components.

• Develop and optimize streaming and batch data workflows.

• Improve reliability and operability (monitoring, alerting, incident prevention/recovery).

• Improve data quality and consistency through schema/contracts and validation mechanisms.

• Diagnose and resolve production issues across service and data layers.

• Drive performance and cost optimization initiatives for large-scale workloads.

• Collaborate across teams to deliver roadmap milestones.

• Mentor engineers and contribute to engineering standards and review culture.

About you

[Minimum qualifications]

• 6+ years of software engineering experience in backend/data-intensive systems.

• Strong proficiency in Go (required).

• Strong proficiency in Python (required).

• Strong proficiency in SQL (required).

• Hands-on experience with distributed data systems (e.g., Kafka, Spark/Databricks).

• Experience operating services/pipelines in cloud production environments (GCP preferred).

• Solid understanding of reliability practices: observability, on-call, incident response.

• Strong communication and ownership; able to drive ambiguous projects to completion.

[Preferred qualifications]

• Experience with Databricks/Delta Lake/Lakehouse architectures.

• Experience with Kubernetes/GKE and Terraform/IaC.

• Experience with data governance (lineage, access control, PII handling).

• Experience with identity/profile systems (entity resolution, merge logic).

• Experience with FinOps/cost optimization for platform workloads.

• Scala/Java experience for Spark ecosystem optimization is a plus.

[Language]

Fluent in English.

Why join us

• Work on high-impact, business-critical data backend systems.

• Own real production outcomes across scale, quality, and cost.

• Collaborate with strong cross-functional teams in AI + data.

• Grow technical leadership while staying hands-on.

Open to overseas candidates/Visa Support
This position is open to based in Taipei, Taiwan or Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

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【 About the role 】
We are looking for a Senior Analytics Engineer to join our centralized data team and take ownership of domain-level data modeling, metric standardization, and dashboard delivery that power both product innovation and business growth.

You will collaborate closely with engineers, product managers, and data scientists — actively participating in product development scrums — and will play a critical role in designing data instrumentation, enabling trustworthy insights, and scaling decision-making infrastructure.

【What You’ll Do 】

• Design and maintain tracking plans across our data ecosystem, including naming conventions, event schemas, and user properties.
• Build and maintain data pipelines in Databricks using Python, Spark, SQL with Delta Live Tables (DLT).
• Define, implement, and document core business metrics (e.g. churn, MRR, engagement, conversions) at the data model layer.
• Own and develop Tableau dashboards, delivering clear and actionable insights to business and product stakeholders.
• Collaborate with Data Scientists to integrate model outputs (e.g. churn scores, segmentation) into production-ready datasets and reporting.
• Support self-service analytics and ensure consistency and clarity in metric definitions.
• Leverage AI tools (e.g. ChatGPT, GitHub Copilot, or internal automation) to enhance productivity and improve modeling workflows.
• Ensure data quality, governance, and documentation across all owned datasets and dashboards.

【 Who you are 】

• 3+ years in an Analytics Engineer, Data Engineer (analytics-focused), or BI Engineer role.
• Hands-on experience of building data pipelines on Databricks or Spark environments with Python.
• Knowledge of ELT process patterns, Dimension modeling, and Medallion architecture.
• Strong SQL and experience building and maintaining gold-level datasets.
• Experience designing and maintaining Tableau dashboards for cross-functional teams.
• Comfortable working directly with engineers on event instrumentation and data validation.
• Familiarity with Mixpanel, Braze, or similar event-based tools (Segment, Amplitude, etc.).
• Able to translate business questions into data products, and ensure consistent metric definitions.
• Interest in leveraging AI tools to optimize data development and analysis workflows.
• Strong problem-solving skills and attention to detail.
• Ability to manage multiple projects simultaneously and prioritize effectively.
• Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

【Benefits】

• Flexible Time-off; work hard and take time off when you need it
• Flexible working hours and work-from-home when needed
• Medical insurance and free yearly health checkup
• Yearly health checkup subsidy for your pets
• 2 free Furbos for each full-time team member
• Company trip and employee outing program
• Internal workshop and external training subsidy for you to expand your skill set and continuously learn and grow.

【 Perks 】

• The rare opportunity to work with an international team from Taiwan HQ and make impact on a global scale
• Join a group of talents who have passion to change pet parents and pet's lives through technology
• The freedom to voice yourself in an open, trusting and transparent working environment
• Quarterly all-hands meetings to update entire organization on company goals, strategic directions and the opportunity to openly ask questions/give feedback
• Bring your fur babies to work in a pet-friendly office space designed to make your pet feel relaxed and comfortable
• Join our volunteer program. We are passionate about giving back to society.
• Various club activities to cultivate interests and have fun with Tomofuners after work!
• A LOT of free snacks and drinks for humans and furkids!
• Comfortable and creative office with kitchen, sofa, and quiet areas. Everyone can find their ideal place to work!
• Afternoon tea time twice a month and monthly birthday parties. We celebrate all the happy moments together.

[Job Overview]
We are looking for an experienced Senior Data Engineer to join our engineering team and play a key role in building and scaling our enterprise data platform. You will design, develop, and maintain high-quality data warehouses and data-driven applications that power analytics, reconciliation, and business decision-making across the organization.
This role requires strong expertise in modern data architectures, pipeline engineering, and data quality management. The ideal candidate combines hands-on technical capability with a deep commitment to reliability, scalability, and governance in a regulated environment.

[Responsibilities]
· Data operations: own day-to-day operations of data platforms/pipelines capacity, stability, upgrades, deployments, and recovery drills to sustain high availability and low latency.
· Data collection: design/manage multi-source ingestion (exchanges, internal and external systems), protocol parsing, and robust retry mechanisms.
· Develop rule-based and statistical data quality checks (completeness, uniqueness, time alignment, anomaly detection, error handling).
· Implement automated remediation, reconciliation workflows, and historical backfilling.
· Establish monitoring and alerting frameworks to ensure trusted, production-grade datasets.
· End-to-End pipelines: plan and maintain scalable ETL/ELT including scheduling, caching, partitioning, modelling, schema evolution, and lineage to support both batch and real-time streaming.
· Enforce data access controls, encryption, auditing, and classification to comply with internal policies and external regulatory requirements (including PII management).
· Apply Infrastructure-as-Code, data versioning, data tests, and CI/CD to improve predictability and reduce manual risk.
· Contribute to embedded GenAI and LLM-powered data applications for enterprise analytics, reconciliation, and internal productivity use cases.
· Partner with analytics and product teams to operationalize AI-driven data solutions.

[Requirements]
· Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field.
· 5+ years of experience in data engineering, data platform architecture, or AI/ML engineering.
· Strong experience with modern cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift).
· Hands-on experience building BI data foundations and supporting GenAI / LLM architectures.
· Proficiency in SQL and workflow orchestration tools (e.g., Airflow), streaming platforms (e.g., Kafka), and pipeline design best practices.
· Solid understanding of data warehouse development lifecycles and dimensional modeling concepts.
· Familiarity with GitLab and CI/CD pipelines.
· Strong debugging, performance tuning, and problem-solving skills.
· Working knowledge of data governance, lineage, privacy, and security frameworks.

About Appier

Appier is a leading SaaS company empowering businesses with cutting-edge artificial intelligence (AI) to drive smarter decision-making. Founded in 2012 with a mission to democratize AI, we transform complex data into actionable insights, making AI accessible and profitable. With 17 offices across APAC, Europe, and the U.S., and listed on the Tokyo Stock Exchange (Ticker: 4180), Appier is at the forefront of AI innovation. Visit www.appier.com for more information.

About the Role

We’re on the lookout for an ambitious and technically outstanding Senior Software Engineer, Machine Learning to join our Enterprise Solution Engineering Team, AIXON. This elite team leverages state-of-the-art ML technologies to solve real-world marketing challenges by integrating omnichannel customer data at scale.

In this role, you’ll be the vital bridge between cutting-edge research and production-grade deployment. You’ll design, build, and optimize scalable, high-performance ML infrastructure—including data pipelines, APIs, monitoring systems, and workflow orchestration—that power transformative AI solutions.

What You’ll Do

• Architect and operate resilient ML job execution frameworks covering training, inference, and post-processing workflows.

• Develop and maintain API services and developer tooling to orchestrate ML workflows on Kubernetes using Argo Workflows, Helm, Terraform.

• Build scalable, efficient batch pipelines with Apache Spark to support large-scale ML training and evaluation.

• Design and maintain robust data infrastructures using Trino, Databricks and other modern database technologies, monitored with Prometheus and Grafana for high availability and observability.

• Develop tooling that streamlines ML experimentation, accelerates production workflows, and empowers cross-functional teams to innovate rapidly.

• Collaborate deeply with ML scientists to transform research prototypes into reliable, scalable, user-facing AI products.

• Lead cloud infrastructure design and operations on GCP, leveraging managed services such as Google Compute Engine (GCE) , Google Kubernetes Engine (GKE) , Cloud Storage, Cloud Functions, Cloud Pub/Sub, Cloud SQL, BigQuery, and more.

• Define and implement CI/CD pipelines with tools like Jenkins, Github Action, or ArgoCD to enable seamless, automated deployments.

• Harness distributed computing and parallel programming principles to optimize system resource utilization and performance.

What You Bring

• Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s degree preferred).

• 5+ years of hands-on experience in ML platform engineering, MLOps, or data infrastructure, deploying enterprise-grade machine learning systems at scale.

• Expert proficiency in Python, Java, or Go, with solid foundations in data structures and algorithm design.

• In-depth experience with cloud environments (AWS or GCP) and cloud-native service management.

• Proven mastery of Docker containers and Kubernetes cluster management, including resource provisioning, autoscaling, and deployment best practices.

• Strong understanding of the ML lifecycle—from training and prediction to evaluation, backtesting, and feedback loops.

• Familiarity with Git workflows and Linux-based development environments.

• Passionate about continual learning and innovation, leveraging AI-powered developer tools like GitHub Copilot and ChatGPT to boost productivity.

What Will Set You Apart

• Experience in the MarTech industry or other customer-centric domains, eager to deliver products that delight users and drive business impact.

• Demonstrated architectural leadership and ownership, skillfully driving complex, cross-team platform initiatives.

• Strong grasp of deep learning fundamentals and end-to-end ML workflow platforms such as Kubeflow, MLflow, or AWS SageMaker.

• Hands-on experience with distributed data processing frameworks like Apache Spark, and pipeline orchestration tools such as Apache Airflow, Argo Workflow, or Luigi.

• Expertise in production-level ML applications, including handling data imbalance, preventing data leakage, and optimizing resource consumption for large-scale training and serving.

• Familiarity with real-time online inference architectures and batch processing trade-offs.

• Enthusiastic adopter of “vibe coding” culture—collaborative, transparent, and always pushing technical excellence together.

• Prior experience building or developing applications related to large language models (LLM), multi-agent LLM systems, or natural language processing (NLP).

Why Join Appier?

At Appier, you’ll stand at the frontier of AI innovation, working alongside world-class engineers and researchers to create products that transform entire industries. Here, your engineering expertise will directly impact millions of users and drive revolutionary advances in marketing technology. If you’re ready to tackle challenging ML infrastructure problems with passion and creativity, Appier is your ultimate playground!

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