FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Data Engineer
A.P. Moller - Maersk. Ingest the world: Design and maintain ingestion frameworks for high-volume, structured and unstructured data-from operational systems, APIs, file drops, and events.
Tech Stack
Tools & technologiesAWSCloudDockerGrafanaKubernetesPrometheusPySparkPythonSparkSQLTerraform
About the role
Key responsibilities & impact- Ingest the world: Design and maintain ingestion frameworks for high-volume, structured and unstructured data-from operational systems, APIs, file drops, and events.
- Support streaming and batch use cases across latency windows.
- Transform at scale: Develop transformation logic using SQL , Python , Spark , and modern declarative tools like dbt or sqlmesh . You’ll handle deduplication, windowing, watermarking, late-arriving data, and more.
- Curate for trust: Collaborate with domain teams to annotate datasets with metadata, ownership, PII classification, and usage lineage.
- Enforce naming standards, partitioning schemes, and schema evolution policies.
- Optimize for the lakehouse: Work within a modern lakehouse architecture -leveraging Delta Lake, S3, Glue, and EMR -to ensure scalable performance and queryability across real-time and historical views.
- Build for observability: Instrument your pipelines with quality checks, cost visibility, and lineage hooks.
- Integrate with OpenMetadata, Prometheus, or OpenLineage to ensure platform reliability and traceability.
- Enable production-readiness: Support deployment workflows via GitHub Actions, Terraform, and IaC patterns.
- Your code will be versioned, testable, and safe for multi-tenant deployments.
- Think platform-first: Everything you build should be reusable. You’ll help codify data engineering standards, create scaffolding for onboarding new datasets, and drive automation over repetition.
Requirements
What you’ll need- Python(PySpark) & SQL — Non-negotiable. Strong working proficiency in both.
- AWS — Solid understanding of AWS services beyond just data engineering (storage, compute, networking, IAM, etc.). Preference for candidates already working within the AWS ecosystem.
- Data Fundamentals & Data Pipeline Optimization — Working knowledge of optimizing pipelines for cost efficiency and resource utilization.
- Interest in working in Platform Engineering
- Platform Engineering Mindset — Must have a genuine interest in platform/infrastructure work, not just pipeline development. Cultural fit on this is important — we don't want drop-offs post-interview.
- Containerization & Orchestration — Conceptual understanding or hands-on experience with Docker and Kubernetes.
- Cloud Migration / Multi-cloud — Experience with cloud migrations or working across multi-cloud environments.
- AI/ML — Any exposure to AI/ML concepts or tooling is a bonus, not a requirement.
- Infrastructure as Code (IaC) — Familiarity with IaC tooling (Terraform, CDK, etc.).
- Observability — Familiarity with tools like Grafana and Prometheus for monitoring and alerting.
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonSQLSparkdbtsqlmeshAWSDockerKubernetesTerraformIaC
Soft Skills
collaborationplatform engineering mindsetcultural fitautomationobservability