Data & AI Solutions Specialist

UARROW PTE. LTD.; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

UARROW PTE. LTD. is seeking a Senior Data Engineer to design, build, and optimize scalable data pipelines using Spark, SQL, Python and cloud services. You will work across data ingestion, processing, warehousing and data lake technologies to deliver production-grade solutions.

The role emphasizes collaboration with architects, data scientists and engineers, with a focus on performance tuning, CI/CD, and containerized deployments in AWS environments.

Qualifications

• 8+ years of experience in Data Engineering / Big Data.
• Strong Python, Java or Scala programming skills.
• Deep experience with Spark/PySpark, SQL, Hadoop and Hive.
• Hands-on AWS cloud experience.
• Experience with data warehouses, data lakes and relational databases.
• Familiarity with Kafka, Airflow, Jenkins, Git and CI/CD practices.
• Knowledge of Databricks, Snowflake or Cloudera is advantageous.

Responsibilities

• Design and maintain scalable batch and real-time data pipelines.
• Develop ETL/ELT workflows for large-scale datasets.
• Create data ingestion solutions using databases, APIs, files and streaming platforms.
• Collaborate with architects, data scientists and engineers to deliver production-ready solutions.
• Implement CI/CD pipelines and containerized deployments using Docker, Kubernetes and OpenShift.
• Integrate Generative AI capabilities into enterprise data applications where required.
• Participate in system design, development, testing, deployment and production support.

Skills

Python

Java

Scala

Spark

SQL

Hadoop

Hive

Kafka

Airflow

Jenkins

Git

Docker

Kubernetes

OpenShift

Databricks

Snowflake

Cloudera

Generative AI

LLMs

LangChain

Education

Bachelor’s or Master’s in CS/IT/Engineering

Tools

AWS

S3

Glue

EMR

Redshift

Kinesis

Lambda

Docker

Kubernetes

OpenShift

Databricks

Snowflake

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.