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Data Engineer CRM
Primary Objectives • Design and maintain scalable data pipelines powering CDP and CRM platforms. • Ensure data quality, consistency, and availability across enterprise systems. • Collaborate with cross-functional teams to deliver reliable, business-driven solutions. • Develop and enhance data mart (Customer Profile, Journey, Event) as CDP/CRM backbone. Main responsibilities • Data Integration & Pipelines: Build and optimize batch/real-time ETL/ELT pipelines across transactional, marketing, CRM/CDP, and external APIs, ensuring scalability and cost efficiency. • Data Modeling & Architecture: Design CDP/CRM data models using medallion architecture, dimensional modeling, data vault, or data mesh; align with governance standards. • Operations & Optimization: Monitor pipelines, troubleshoot, automate alerts, and drive continuous performance improvements. • Others: + Maintain documentation, conduct code reviews, and promote knowledge sharing. + Support ad-hoc data needs and troubleshoot data issues. + Research and adopt emerging data technologies to enhance platform capabilities
1. Education level • Bachelor’s degree in information technology, business, or related field, or equivalent combination of education and experience required 2. Knowledge & Experience • 3+ years of experience as a Data Engineer with strong ETL/ELT pipeline development. • Advanced proficiency in SQL and data modeling for large-scale systems. • Hands-on experience with cloud data platforms (Databricks, BigQuery, Redshift, Snowflake or Spark-based solutions). • Familiarity with CDP/CRM ecosystems and integration with marketing platforms (Facebook Ads, Google Analytics, Criteo, RTB House). • Practical experience with workflow orchestration (DBT, Airflow, or equivalent). 3. Technical skills • Advanced SQL (queries, tuning, optimization); strong Python (data processing, automation, APIs); Scala/Java for Spark is a plus. • Experience with Databricks, Spark, or similar; cloud data warehouses (Databricks, BigQuery, Synapse, Snowflake); orchestration tools (DBT, Airflow, etc.). • Building ETL/ELT pipelines for structured/unstructured data; real-time streaming (Kafka, Pub/Sub, EventHub); CRM/CDP and API integrations. • Knowledge of lineage, catalog, and metadata management; hands-on with frameworks like Great Expectations or Soda. • CI/CD for data engineering (Git, GitHub Actions, Azure DevOps); exposure to Docker/Kubernetes. 4. Soft skills • Effective communicator, able to bridge technical and business stakeholders. • Analytical mindset with strong troubleshooting skills; proactive in identifying risks, gaps, and proposing solutions. • Curious and open to new technologies, eager to adopt best practices and experiment with new tools for efficiency.
Benefits will be shared in details for successful candidates
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