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APAR TECHNOLOGIES PTE. LTD.

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Data Engineer

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Summary

Hands-on Data Engineer designing, building, and optimizing batch and streaming data pipelines on the Databricks Lakehouse platform using PySpark, Delta Lake, and Unity Catalog. Delivers reliable, analytics-ready data with medallion architecture, data governance, and CI/CD practices for analysts and business teams.

RoleOverview

Weare looking for a hands-on Data Engineer to design, build, and optimizescalable data pipelines on the Databricks Lakehouse platform. The role involvescollaborating with analysts, data scientists, and business teams to deliverreliable, analytics-ready data assets.

KeyResponsibilities
  • Build and maintain batch and streaming data pipelines using PySpark and Databricks .
  • Implement medallion architecture (Bronze, Silver, and Gold layers).
  • Optimize Spark jobs for performance and cost efficiency.
  • Ensure data quality, lineage, and governance using Unity Catalog and automated validation.
  • Integrate data from APIs, databases, files, and event streams.
  • Implement CI/CD, version control, and workflow orchestration.
  • Monitor, troubleshoot, and document data pipelines and data models.
  • Collaborate with stakeholders to translate business requirements into technical solutions.
RequiredQualifications
  • 3-5 years of hands-on data engineering experience.
  • Strong PySpark skills, including DataFrames, Spark SQL, UDFs, and performance tuning .
  • Proven experience building production pipelines on Databricks.
  • Working knowledge of Delta Lake, including ACID transactions, time travel, merge/upsert, and schema evolution .
  • Advanced SQL and strong Python programming skills .
  • Experience with Azure, AWS, or GCP cloud platforms.
  • Understanding of data modelling, Lakehouse architecture, and ETL/ELT patterns.
  • Familiarity with Git and CI/CD practices.
PreferredQualifications
  • Databricks Data Engineer Associate or Professional certification.
  • Experience with Unity Catalog, Auto Loader, and Structured Streaming.
  • Familiarity with Airflow, Azure Data Factory, or Databricks Workflows.
  • Knowledge of Terraform and testing frameworks such as pytest.
  • Exposure to Kafka, Event Hubs, dbt, or MLOps workflows.
  • Experience working in regulated or enterprise environments.
KeySkills
  • PySpark and Python
  • Databricks and Delta Lake
  • Advanced SQL
  • Data Pipeline Development
  • Medallion Architecture
  • Cloud Data Engineering
  • Data Quality and Governance
  • CI/CD and Git
  • Spark Performance Tuning
DesiredAttributes
  • Strong problem-solving and debugging skills.

EA Number: 11C4879

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