Data Engineering Consultant

re-zoo-me; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

re-zoo-me is seeking a Senior Data Engineer to design and optimize scalable data pipelines using Spark/PySpark and cloud technologies. You will work closely with architects, data scientists and engineers to deliver robust data processing solutions.

The role requires 8+ years in data engineering, strong Python/Java/Scala skills, and experience with Spark, Hadoop, Hive and AWS. Familiarity with data lakes, warehouses and AI tooling is a plus.

Qualifications

• 8+ years in Data Engineering / Big Data environments.
• Strong programming in Python, Java and/or Scala.
• Hands-on Spark/Hadoop/Hive experience and data pipeline design.
• Cloud experience, especially AWS, and data warehousing skills.

Responsibilities

• Design, build and optimize scalable batch/real-time data pipelines.
• Develop ETL/ELT processes for large-scale data workflows.
• Create data ingestion solutions via databases, APIs, files and streaming.
• Implement CI/CD for data apps and support containerized deployments.
• Collaborate with architects, data scientists and stakeholders to deliver production-ready solutions.
• Explore Generative AI/NLP integrations and AI APIs where applicable.

Education

Bachelor's/Master's in CS/IT/Engineering

Tools

Spark/PySpark

Hadoop

Hive

Kafka

Airflow

Docker

Kubernetes

OpenShift

Databricks

Snowflake

Cloudera

AWS

LangChain

Job description

Job Description

We are looking for a highly skilled and experienced Senior Data Engineer to design, develop and optimize scalable data engineering solutions using Big Data, cloud and modern AI technologies.

The successful candidate will be responsible for developing high-performance data pipelines, data ingestion frameworks and data processing solutions, while working closely with architects, business stakeholders, data scientists and engineering teams.

Key Responsibilities

• Design, develop and maintain scalable batch and real-time data pipelines using Apache Spark/PySpark, SQL and Python.
• Develop and optimize ETL/ELT pipelines for large-scale data processing.
• Build data ingestion solutions using databases, APIs, files and streaming platforms.
• Work with AWS services including S3, Glue, EMR, Redshift, Kinesis, Lambda and DynamoDB.
• Develop and optimize data processing solutions using Hadoop, Hive, Spark, Kafka, Cloudera and Databricks.
• Perform SQL and Spark performance tuning and optimize large-scale data processing workloads.
• Design and implement data models, data warehouses and data lake solutions.
• Develop CI/CD pipelines and support containerized deployments using Docker, Kubernetes and OpenShift.
• Integrate Generative AI and NLP capabilities into enterprise data applications where required.
• Develop solutions using LLM frameworks, RAG, vector databases and AI APIs.
• Collaborate with solution architects, data scientists, software engineers and business stakeholders to deliver production-ready solutions.
• Participate in system design, development, testing, deployment and production support.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering or a related field.
• Minimum 8 years of relevant experience in Data Engineering / Big Data, with strong experience in large-scale data processing.
• Strong programming experience in Python, Java and/or Scala.
• Strong hands‑on experience with Apache Spark/PySpark, SQL, Hadoop and Hive.
• Experience with cloud platforms, particularly AWS.
• Experience with data warehouses, relational databases and data lake technologies.
• Experience with Kafka, Airflow, Jenkins, Git and CI/CD practices.
• Experience with Docker, Kubernetes or OpenShift is advantageous.
• Knowledge of Databricks, Snowflake and Cloudera is advantageous.
• Exposure to Generative AI, LLMs, RAG, LangChain/LangGraph and vector databases is an advantage.
• Strong analytical, problem‑solving and communication skills.

Technical Skills

Python, Java, Scala, PySpark, Apache Spark, SQL, Hadoop, Hive, Kafka, Databricks, Cloudera, AWS, S3, Glue, EMR, Redshift, Kinesis, Lambda, Airflow, Jenkins, Docker, Kubernetes, OpenShift, Snowflake, MongoDB, Oracle, PostgreSQL and Generative AI technologies.