Senior Data Engineer - Government Project

NTT SINGAPORE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

NTT SINGAPORE PTE. LTD. seeks a Senior Data Engineer to design, build, productionise and operate trusted data pipelines on the SWDA data platform using AWS and Databricks. You will provide technical leadership across ingestion, transformation, modelling, quality, lineage, orchestration and performance.

The role emphasises CRISPr principles—comprehensive, robust and scalable data systems—to ensure data remains dependable for analytics, reporting and ML workloads across the organization.

Qualifications

• Degree in CS/SE/Data Engineering or related field.
• 6–10 years of data/software engineering experience with production pipelines.
• Experience leading data services architecture and standards.

Responsibilities

• Lead design and delivery of batch and streaming pipelines across AWS and Databricks.
• Design Bronze/Silver/Gold layers and reusable data products with ownership and definitions.
• Implement reliable ingestion from databases, files, APIs and streams.
• Define dashboards, monitoring and reliability metrics for pipelines.
• Collaborate with stakeholders to translate analytics needs into data specs.

Skills

Python

PySpark

SQL

Databricks

Apache Spark

Delta Lake

AWS data engineering

Lakehouse design

Change Data Capture

Data quality

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

Apache Spark

Delta Lake

AWS stack (S3, Glue, Lake Formation, IAM, KMS)

Job description

Role Overview

The Senior Data Engineer sits within the DSAD Platform and Architecture Team and is responsible for designing, building, productionising and operating trusted data pipelines and reusable data products on the SWDA data platform using AWS and Databricks. The role will provide technical leadership across data ingestion, transformation, modelling, quality, lineage, orchestration, observability and performance, applying CRISPr principles of comprehensiveness, robustness and scalability so that data remains dependable for analytics, reporting and ML/AI workloads.

Key Responsibilities

• Data Pipeline Architecture and Delivery: Lead the design and implementation of maintainable batch and streaming pipelines that ingest, validate, transform and publish structured, semi-structured and unstructured data across AWS and Databricks.
• Lakehouse and Data Product Engineering: Design Bronze, Silver and Gold data layers using Delta Lake or equivalent patterns; develop reusable domain data models, curated datasets and data products with explicit ownership, definitions and service expectations.
• Source Integration and Incremental Processing: Implement reliable ingestion from databases, files, APIs and event streams using full-load, incremental, change-data-capture and replayable processing patterns appropriate to each source.
• Data Quality and Contracts: Establish schema enforcement, data contracts, automated validation, reconciliation and anomaly checks covering completeness, validity, consistency, uniqueness, timeliness and referential integrity.
• Orchestration and Deployment Automation: Build parameterised workflows using Databricks Workflows or Lakeflow and relevant AWS services; integrate automated testing, infrastructure as code and CI/CD controls across development, test and production environments.
• Metadata, Lineage and Governance: Implement technical metadata, business definitions, end-to-end lineage, classification, ownership and access controls using Unity Catalog, AWS Glue Data Catalog, Lake Formation or approved platform capabilities.
• Pipeline Monitoring and Reliability: Define dashboards, alerts, logging, operational metrics and runbooks for pipeline health, freshness, data-quality failures, latency, throughput and downstream impact; lead troubleshooting and root-cause analysis.
• Performance and Cost Optimisation: Optimise Spark and SQL workloads, file sizes, partitioning, clustering, caching, compaction, cluster configuration and workload scheduling to improve runtime, reliability and AWS/Databricks cost efficiency.
• Security and Data Protection: Apply least-privilege IAM, encryption, secrets management, environment separation, audit logging, sensitive-data controls and secure data-sharing patterns in line with applicable agency requirements.
• Technical Leadership and Quality: Set engineering standards, review designs and code, mentor engineers, manage technical debt and promote reusable components rather than project-specific pipelines.
• Stakeholder Collaboration: Work with data owners, stewards, analysts, data scientists, architects, product teams and governance stakeholders to translate analytical and operational needs into implementable data specifications.
• Documentation and Knowledge Transfer: Maintain architecture decisions, source-to-target mappings, data models, contracts, operating procedures and support documentation, and transfer knowledge to the internal team throughout the engagement.

Requirements & Qualifications

A. Educational Qualification & Experience

Education: Degree in Computer Science, Software Engineering, Data Engineering, Information Systems or a related technical discipline.

Experience: 6-10 years of relevant data or software engineering experience , including hands‑on design and operation of production data pipelines and at least 2 years working with cloud‑based data platforms. Candidates should demonstrate technical leadership through architecture, engineering standards, code review, mentoring or ownership of critical data services.

B. Technical & Professional Competencies

Essential (Must-Have)

• Strong Python, PySpark and SQL capability, including distributed processing, complex transformations, query optimisation, debugging and automated testing.
• Hands‑on experience with Databricks, Apache Spark and Delta Lake for production‑scale batch or streaming data engineering.
• Hands‑on AWS data engineering experience with a relevant mix of S3, Glue, Lake Formation, IAM, KMS, Lambda, Step Functions, EventBridge, CloudWatch, Redshift and MSK/Kinesis.
• Experience designing lakehouse or data‑lake architectures, including layered data patterns, dimensional or domain data modelling and reusable curated datasets.
• Experience implementing incremental ingestion, change data capture, idempotency, late‑arriving data handling, backfills, replay and recovery.
• Experience implementing data quality, schema validation, reconciliation, metada