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Data Engineer (AWS, Central Location) at re-zoo-me – Singapore | Jobyssey

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Data Engineer (AWS, Central Location)

Data Engineer (AWS, Central Location)

re-zoo-me · Singapore, Singapore · 2 weeks ago

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Category: Data engineering
Experience: 7+ years

airflow analytics aws bigquery ci-cd cloud data-engineering data-ingestion data-lineage data-pipelines data-quality data-science

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Senior Data Engineer designing, building and operating enterprise-grade data platforms on AWS for analytics, data science and digital products. Day to day: data pipelines, models, warehouses/lakehouses and engineering standards using Python, SQL, AWS data services (Redshift/Snowflake/Databricks/BigQuery), plus Airflow, dbt and Spark.

Our client is looking for a Senior Data Engineer to design, build and operate enterprise-grade data platforms supporting analytics, data science and digital products. The role will contribute to data architecture, engineering standards and technical decision-making in a complex enterprise environment.

Responsibilities

Design, build and maintain scalable data architectures and pipelines for data ingestion, transformation and delivery.
Develop robust data models and reusable data capabilities for analysts, data scientists and applications.
Improve the reliability, security, performance, observability and maintainability of data platforms.
Evaluate technologies and architectural approaches, making sound technical trade-offs and contributing to engineering standards.
Champion modern engineering practices including automated testing, code reviews, CI/CD and Infrastructure-as-Code.
Collaborate with engineers, Product Managers, Data Scientists and analysts, while providing technical leadership, mentoring and knowledge sharing.

Requirements

7+ years of experience in software/data engineering with strong Python and SQL skills.
Experience with AWS and modern data platforms such as Redshift, Snowflake, Databricks or BigQuery.
Strong experience in enterprise data architecture and engineering, including production data systems.
Hands-on experience designing data pipelines, data models, data warehouses, data lakes or lakehouse architectures.
Experience with data orchestration, transformation and modelling.
Strong understanding of production engineering practices including testing, CI/CD, monitoring, troubleshooting and data quality.

Good to Have

Experience with Apache Airflow, dbt and/or Apache Spark.
Strong knowledge of AWS data services and cloud infrastructure.
Experience with Tableau, Power BI or similar BI tools.
Knowledge of Infrastructure-as-Code, data observability, metadata, data catalogues or data lineage.
Experience working with sensitive or regulated data.

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