Senior Data Engineer AWS / Databricks / PySpark

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 .