Data Engineer

2 weeks ago

Singapore HORIZON SOFTWARE PTE. LTD. Full time
Roles & Responsibilities

Key Responsibilities
  • Design, develop, and maintain data pipelines, ETL/ELT processes, and data integration workflows.
  • Architect and optimize data lakes, data warehouses, and streaming platforms.
  • Work with structured, semi-structured, and unstructured data at scale.
  • Implement real-time and batch data processing solutions.
  • Collaborate with Data Scientists, Analysts, and Business stakeholders to deliver high-quality data solutions.
  • Ensure data security, lineage, governance, and compliance across platforms.
  • Optimize queries, data models, and storage for performance and cost efficiency.
  • Automate processes and adopt DevOps/DataOps practices for CI/CD in data engineering.
  • Troubleshoot complex data-related issues and resolve production incidents.
  • Mentor junior engineers and contribute to technical strategy and best practices.
Technical Skills (Must-Have Tough Requirements)

Programming & Scripting

  • Proficiency in Python, Scala, or Java for data engineering.
  • Strong SQL skills (query optimization, tuning, advanced joins, window functions).

Big Data & Distributed Systems

  • Expertise with Apache Spark, Hadoop, Hive, HBase, Flink, Kafka.
  • Hands-on with streaming frameworks (Kafka Streams, Spark Streaming, Flink).

Cloud & Data Platforms

  • Deep knowledge of AWS (Redshift, Glue, EMR, Athena, S3, Kinesis),

    or Azure (Synapse, Data Factory, Databricks, ADLS),

    or GCP (BigQuery, Dataflow, Pub/Sub, Dataproc).
  • Experience with Snowflake, Databricks, or Teradata.

ETL/ELT & Orchestration

  • Strong experience with Airflow, Luigi, Azkaban, Prefect.
  • ETL tools like Informatica, Talend, SSIS.

Data Modeling & Storage

  • Experience with Data Lakes, Data Warehouses, and Lakehouse architectures.
  • Knowledge of Star Schema, Snowflake Schema, Normalization/Denormalization.

DevOps & Automation

  • Proficiency in CI/CD (Jenkins, GitLab, Azure DevOps) for data pipelines.
  • Experience with Docker, Kubernetes, Terraform, Ansible for infrastructure automation.

Other Tough Skills

  • Strong knowledge of Data Governance, MDM, Data Quality, Metadata Management.
  • Familiarity with Graph Databases (Neo4j), Time-Series Databases (InfluxDB, TimescaleDB).
  • Understanding of machine learning data pipelines (feature engineering, model serving).
Qualifications
  • Bachelor's/Master's degree in Computer Science, Data Engineering, or related field.
  • 7–10 years of experience in data engineering or big data development.
  • At least 2–3 large-scale end-to-end data platform implementations.
  • Preferred Certifications:

    AWS Certified Data Analytics – Specialty

    Google Professional Data Engineer

    Databricks Certified Data Engineer
Tell employers what skills you have

Management Skills
Leadership
Interpersonal Skills
Problem Solving
Administration
Communication Skills
Team Lead
Team Leader
Team Player
Scheduling

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