Senior Data Engineer | Contract | Hybrid Working Arrangement

NEXBRIDGE RECRUITMENT PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

NEXBRIDGE RECRUITMENT PTE. LTD. is seeking an experienced Data Engineer to lead design and implementation of scalable batch and streaming data pipelines on AWS and Databricks. You will develop Bronze-Silver-Gold data layers, enforce data contracts, and drive data governance across interconnected teams.

The role requires hands-on expertise with Databricks, Spark, Delta Lake and cloud data services, plus strong coding in Python and SQL to deliver reliable data products and dashboards.

Qualifications

• 6 years of relevant data or software engineering experience with production data pipelines
• Hands-on design and operation of cloud-based data platforms (e.g. AWS)
• Strong Python, PySpark and SQL capabilities including distributed processing and automated testing
• Experience with Databricks, Apache Spark and Delta Lake in production
• Experience with data lake architectures, data contracts and data governance

Responsibilities

• Lead design and implementation of batch and streaming pipelines ingesting diverse data
• Design Bronze, Silver and Gold data layers using Delta Lake
• Implement ingestion from databases, files, APIs and event streams
• Establish data quality checks, contracts, lineage and metadata management
• Build parameterised workflows using Databricks Workflows or Lakeflow
• Define observability dashboards, alerts and runbooks for pipeline health and data quality
• Optimize Spark/SQL workloads and cost efficiency on AWS/Databricks
• Apply security controls and least-privilege access for data sharing
• Mentor engineers and promote reusable data components
• Collaborate with data owners, governance and platform teams to translate needs into specs

Skills

Python

PySpark

SQL

Databricks

Apache Spark

Data modeling

Leadership

Education

Bachelor's degree in Computer Science/Software Engineering/Data Engineering or related

Tools

Databricks

Delta Lake

Lake Formation

Delta Live Tables

Structured Streaming

Job description

Job Responsibilities

• 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
• Design Bronze, Silver and Gold data layers using Delta Lake or equivalent patterns, and develop reusable domain data models, curated datasets and data products with explicit ownership, definitions and service expectations
• Implement reliable ingestion from databases, files, APIs and event streams using full-load, incremental, change-data-capture and replayable processing patterns suited to each source
• Establish schema enforcement, data contracts, automated validation, reconciliation and anomaly checks covering completeness, validity, consistency, uniqueness, timeliness and referential integrity
• Build parameterised workflows using Databricks Workflows or Lakeflow and relevant AWS services, with automated testing, infrastructure as code and CI/CD controls across development, test and production environments
• Implement technical metadata, business definitions, end-to-end lineage, classification, ownership and access controls using Unity Catalog, AWS Glue Data Catalog, Lake Formation or other approved platform capabilities
• Define dashboards, alerts, logging, operational metrics and runbooks for pipeline health, freshness, data-quality failures, latency, throughput and downstream impact, and lead troubleshooting and root-cause analysis
• 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
• 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
• Set engineering standards, review designs and code, mentor engineers, manage technical debt and promote reusable components over project-specific pipelines
• Work with data owners, stewards, analysts, data scientists, architects, product teams and governance stakeholders to translate analytical and operational needs into implementable data specifications
• 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

Job Requirements

• Degree in Computer Science, Software Engineering, Data Engineering, Information Systems or a related technical discipline
• 6 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
• Demonstrated technical leadership through architecture, engineering standards, code review, mentoring or ownership of critical data services
• 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, metadata and lineage as automated parts of production pipelines
• Experience with workflow orchestration, Git‑based development, CI/CD, infrastructure as code (e.g. Terraform) and environment promotion controls
• Ability to implement pipeline observability, operational dashboards, alerting, runbooks, incident diagnosis and service recovery
• Working knowledge of cloud security, least‑privilege access, network boundaries, encryption, secret management, data classification and audit logging
• Ability to review technical designs and code, establish engineering standards, and guide less‑experienced engineers
• Strong analytical and systems‑thinking capability, with the discipline to identify failure modes, edge cases and operational dependencies before production deployment
• Clear written and verbal communication, including the ability to explain data architecture, quality, lineage, security, cost and delivery trade‑offs to technical and management audiences
• Collaborative working style across data engineering, platform engineering, analytics, data science, architecture, cybersecurity and governance teams
• Able to translate ambiguous business needs into clear data definitions, contracts, acceptance criteria and incremental delivery plans
• Strong ownership and production mindset, including careful documentation, support readiness and follow‑through on incidents and technical debt
• Able to mentor engineers constructively, raise engineering standards and build sustainable in-house capability
• Experience with Databricks Unity Catalog, Lakeflow or Delta Live Tables, Auto Loader, Structured Streaming, Delta Sharing and Databricks Asset Bundles will be an advantage
• Experience with Apache Airflow, dbt, Kafka, Amazon MSK/Kinesis, AWS DMS or equivalent ingestion and orchestration technologies will be an advantage
• Experience designing data products, data contracts, semantic or context layers, master/reference data patterns or data mesh‑aligned operating models will be an advantage
• Experience supporting feature engineering, ML training datasets, feature stores or MLOps pipelines on Databricks and AWS will be an advantage
• Experience in Singapore public‑sector environments, including IM8‑aligned controls, data classification, VAPT, system assurance or governed cross‑agency data sharing, will be an advantage
• Certifications such as Databricks Certified Data Engineer Professional, AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect – Associate/Professional will be an advantage