Senior Data Engineer AWS / Databricks / PySpark

astek singapore innovation technology pte. ltd.; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

astek singapore innovation technology pte. ltd. seeks a Senior Data Engineer to design, build and optimize scalable data pipelines on AWS and Databricks, enabling analytics and AI workloads.

You will lead data lakehouse architectures, implement governance, quality checks and CI/CD for data products, mentoring juniors and collaborating with scientists and business stakeholders in a dynamic cloud environment.

Qualifications

• 6–10 years of data/software engineering experience.
• Experience with cloud-based data platforms (AWS, Databricks).
• Strong knowledge of ETL/ELT, CDC, data quality, metadata and lineage.
• Experience with data governance and security concepts.

Responsibilities

• Design, develop and optimise scalable batch and streaming data pipelines.
• Lead technical design and code reviews; mentor engineers.
• Build and maintain lakehouse architectures (Bronze/Silver/Gold).
• Establish automated data quality checks and lineage.
• Collaborate with data scientists, analysts and stakeholders.

Skills

Technical leadership

Mentoring

Collaboration

Data governance

Problem solving

Education

Bachelor’s degree in CS / SE / Data Eng / Info Systems

Tools

Python

PySpark

SQL

Apache Spark

Databricks

Delta Lake

AWS services (S3, Glue, IAM, Redshift, Lambda)

CI/CD

Terraform

Job description

Senior Data Engineer – AWS / Databricks / PySpark

Role Overview

We are seeking an experienced Senior Data Engineer to design, develop and maintain scalable data pipelines and reusable data products on an AWS and Databricks-based data platform. The role involves technical leadership in data engineering, lakehouse architecture, data quality, governance and performance optimisation to support analytics, reporting and AI/ML workloads.

Key Responsibilities

• Design, develop and optimise scalable batch and streaming data pipelines using AWS, Databricks, Python, PySpark and SQL.

• Build and maintain lakehouse architectures using Delta Lake, including Bronze, Silver and Gold data layers, curated datasets and reusable data products.

• Implement data ingestion from databases, APIs, files and event streams, including incremental loading, Change Data Capture (CDC), backfills and recovery.

• Establish automated data quality checks, schema validation, reconciliation, metadata management and data lineage.

• Develop and automate data workflows using Databricks Workflows, Lakeflow and AWS services, incorporating CI/CD, automated testing and infrastructure as code.

• Implement data governance, access controls and security measures using Unity Catalog, AWS Glue Data Catalog, Lake Formation and IAM.

• Monitor pipeline performance, troubleshoot production issues and optimise Spark/SQL workloads for scalability, reliability and cost efficiency.

• Lead technical design and code reviews, establish engineering standards and mentor junior engineers.

• Collaborate with data scientists, analysts, architects and business stakeholders to deliver reliable data solutions.

• Maintain technical documentation, data models, operational procedures and knowledge transfer materials.

Requirements

• Degree in Computer Science, Software Engineering, Data Engineering, Information Systems or a related discipline.

• 6–10 years of data/software engineering experience, including at least 2 years working with cloud-based data platforms.

• Strong hands-on experience with Python, PySpark, SQL, Apache Spark, Databricks and Delta Lake.

• Proficiency in AWS data services such as S3, Glue, Lake Formation, Lambda, Step Functions, EventBridge, CloudWatch, Redshift, MSK/Kinesis, IAM and KMS.

• Experience designing data lake/lakehouse architectures, data models and production-grade batch or streaming pipelines.

• Strong understanding of ETL/ELT, CDC, incremental ingestion, data quality, schema validation, metadata and lineage.

• Experience with workflow orchestration, Git, CI/CD, automated testing and infrastructure as code, preferably Terraform.

• Knowledge of data pipeline monitoring, observability, troubleshooting and performance optimisation.

• Familiarity with cloud security, encryption, access management and data governance.

• Proven ability to provide technical guidance, conduct code reviews and mentor engineers.

Good to Have

• Experience with Databricks Unity Catalog, Lakeflow, Delta Live Tables, Auto Loader, Structured Streaming, Delta Sharing and Databricks Asset Bundles.

• Familiarity with Apache Airflow, dbt, Kafka, AWS MSK/Kinesis and AWS DMS.

• Knowledge of data contracts, data products, semantic layers, master/reference data and data mesh concepts.

• Experience supporting ML/AI data pipelines, feature engineering, feature stores or MLOps.

• Exposure to Singapore public-sector environments, including IM8, VAPT, data classification and cross-agency data governance.