Senior Data Engineer | Contract | Hybrid Working Arrangement
Compensation
SGD 6K–SGD 9K / mo
Location
Singapore
Work setup
Not specified
Seniority
Senior
Employment
Contract
Experience
2+ years
Category
Data engineering
Posted
Skills
acceptance-criteria
airflow
analytics
api
aws
aws-glue
ci-cd
cloud
cloud-security
cloudwatch
cybersecurity
data-engineering
data-lake
data-mesh
data-modeling
data-pipelines
data-quality
data-science
databricks
dbt
delta-lake
eventbridge
feature-engineering
git
iam
infrastructure-as-code
kafka
kinesis
lakehouse
lambda
machine-learning
mlops
observability
pyspark
python
redshift
root-cause-analysis
s3
secrets-management
spark
sql
terraform
test-automation
unity
unity-catalog
workflow-orchestration
Full 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
Interested candidates are invited to apply via the “Apply Now” button.
We regret that only shortlisted candidates will be notified.
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