Senior Data Engineer

JOB DESCRIPTION

Primary Objectives

Data engineers design and maintain scalable data pipelines powering CDP and CRM platforms, ensuring data quality, consistency, and availability across enterprise systems. They collaborate with cross-functional teams to deliver reliable, business-driven solutions and 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, Big Query, 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, Big Query, Synapse, Snowflake); orchestration tools (DBT, Airflow, etc.).
• Building ETL/ELT pipelines for structured/unstructured data; real-time streaming (Kafka, Pub/Sub, Event Hub); 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, Git Hub Actions, Azure Dev Ops); 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.