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Ngành nghề
CNTT - Phần mềm
Mức lương
Thương lượng
Địa điểm
Thành phố Hồ Chí Minh, Hồ Chí Minh
So sánh mức lương? Mức lương ngành CNTT - Phần mềm →
Tổng quan
- Data Integration & Workflow Automation
- Lead the design and implementation of enterprise data integrations between Antsomi and customer systems.
- Design and build automated workflows using n8n, Apache Airflow, or similar workflow orchestration platforms.
- Design and operate reliable ETL/ELT workflows and production data pipelines.
- Integrate data through REST APIs, Webhooks, databases, SFTP, batch files, cloud storage, and other integration methods.
- Define appropriate integration patterns including API-based, scheduled, batch, file-based, and event-driven integrations.
- Design workflow scheduling, dependencies, retries, timeout handling, logging, alerting, and recovery mechanisms.
- Define data extraction, transformation, mapping, and loading logic across multiple enterprise systems.
- Work with enterprise data domains such as Customer, Transaction, Product, Subscription, Loyalty, Campaign, and Behavioral/Event data.
- Lead data migration and historical data ingestion when required.
- Review existing integrations and recommend improvements for reliability, scalability, maintainability, and operational efficiency.
- Own complex integration delivery from Design → Build → Test → Go-live → Monitor → Optimize.
- Data Mapping, Quality & Governance
- Analyze complex source system data models and define source-to-target mappings, data dictionaries, transformation rules, and integration specifications.
- Define and implement data validation and reconciliation frameworks.
- Establish data quality rules covering completeness, accuracy, consistency, duplication, validity, and freshness.
- Design reusable and automated data quality checks.
- Lead reconciliation between customer source systems, Antsomi platforms, and downstream reporting.
- Investigate complex data discrepancies across multiple systems and integration layers.
- Support customer identity and Customer 360 data validation across multiple sources.
- Identify systemic data quality issues and recommend corrective and preventive actions.
- Define standards for data mappings, KPI definitions, validation rules, and data documentation.
- Contribute to Data Health and Integration Health assessment frameworks for enterprise customers.
- BI & Analytics
- Lead discussions with customers and Business Solution Consultants to understand business objectives, KPIs, reporting requirements, and analytical questions.
- Translate business requirements into clear metrics, dimensions, calculation logic, analytical datasets, and dashboard requirements.
- Design and build dashboards and reports using appropriate BI and visualization tools.
- Write advanced SQL for data preparation, aggregation, analysis, validation, and troubleshooting.
- Design analytical datasets and reporting structures that support scalable reporting.
- Define and validate KPI calculation logic across source systems, CDP, and reporting outputs.
- Perform analysis across areas such as:
- Customer & Customer 360
- Revenue & Transactions
- Products & Categories
- Customer Segmentation
- Campaign & Journey Performance
- Loyalty & Rewards
- Subscription
- Customer Behavior & Engagement
- Investigate and resolve discrepancies between dashboards, CDP data, analytical datasets, and customer source systems.
Yêu cầu
- Experience with BI and visualization platforms such as Power BI, Looker Studio, Tableau, Metabase, or similar technologies.
- Experience with databases and analytical platforms such as BigQuery, PostgreSQL, MySQL, Snowflake, StarRocks, or similar technologies.
- Experience with AWS, GCP, or Azure.
- Experience with Customer Data Platforms (CDP), CRM, Marketing Automation, Ecommerce, Loyalty, ERP, POS, Billing, or similar enterprise platforms.
- Experience with technologies such as Kafka, Airbyte, dbt, Apache NiFi, Fivetran, or similar data and integration platforms.
- Strong experience integrating multiple third-party SaaS platforms through APIs and Webhooks.
- Experience with authentication and security mechanisms such as OAuth 2.0, JWT, API Keys, and related API security concepts.
- Experience with Python for data processing, analysis, automation, or troubleshooting.
- Understanding of event-driven architecture, queues, streaming, and asynchronous processing.
- Understanding of data modeling, dimensional modeling, and analytical data structures.
- Understanding of customer identity, identity resolution, Customer 360, segmentation, and behavioral data.
- Knowledge of data governance, data quality management, or master data concepts.
- Experience working in a B2B SaaS, Professional Services, System Integration, or enterprise technology environment.
- Experience mentoring junior data or integration team members.
- What Makes You Successful in This Role
- End-to-End Data Thinking
- You understand the complete data lifecycle — from source systems and integrations through transformation, quality, reporting, and business consumption.
- Integration Architecture Thinking
- You do not simply connect System A to System B. You consider reliability, scalability, dependencies, failure scenarios, maintainability, and operational impact.
- Automation Mindset
- You continuously look for opportunities to replace repetitive implementation and operational activities with reusable, automated workflows.
- Data & Analytical Thinking
- You care not only about whether data arrives successfully, but also whether it is correct, meaningful, and capable of answering business questions.
- Technical Depth
- You are comfortable going deep into APIs, SQL, workflows, databases, transformations, dashboards, and production issues when necessary.
- Root-Cause Ownership
- You trace problems across dashboards, datasets, pipelines, integration layers, and source systems until the actual root cause is identified and resolved.
- Business Understanding
- You understand what Customer, Transaction, Product, Loyalty, Campaign, Subscription, and Behavioral data mean to the business and how they should be used.
- Customer Consulting
- You can understand a customer's data ecosystem, challenge requirements, lead data discussions, and recommend practical solutions.
- Leadership
- You improve not only your own work but also the capability of the broader team through mentoring, reviews, reusable frameworks, and best practices.
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