Sr.Data Engineer

What We’re Building – and Why You Should Care

GDA is Maersk’s bold leap into the future of data and AI. It’s not just a platform-it’s a transformation of how the world’s largest integrated logistics company turns its operational data into strategic intelligence. Think: real-time insights on vessel ETA and carbon emissions, metadata-driven supply chain automation, and retrieval-augmented copilots that advise planners and operators. Our data engineers don’t just build pipelines-they shape the very foundation that powers AI-native logistics.

As a data engineer in the GDA-FBM team, you’ll help modernize and operationalize Maersk’s global data estate. You’ll craft reusable, observable, and intelligent pipelines that enable ML, GenAI, and domain-specific data products across a multi-cloud environment. Your code won’t just move data-it’ll move trade.

What You'll Be Doing

• 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.
• Strong expertise building data modelling and dimensional modelling. Experience in SSAS/AAS.
• Experience in Azure tech stack like ADF, ADB.
• Build for observability: Instrument your pipelines with quality checks, cost visibility, and lineage hooks. Integrate with Open Metadata
  , Prometheus
  , or Open Lineage to ensure platform reliability and traceability.
• Enable production-readiness: Support deployment workflows via Git Hub 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.

What We’re Looking For

• Strong foundation in data engineering: You know your way around distributed systems
  , columnar storage formats (Parquet, Avro),
  data lake performance tuning
  , and schema evolution
  .
• Hands-on cloud experience: You’ve worked with AWS-native services like Glue
  , EMR
  , Athena
  , Lambda
  , and object storage (S3). Bonus if you’ve used Databricks
  , Snowflake
  , or Trino
  .
• Modern engineering practices: Familiarity with Git Ops
  , containerized workflows (Docker, K8s), and CI/CD pipelines for data workflows.
  
  Experience with
  Terraform and IaC is highly valued.
• Programming proficiency: Fluency in Python and SQL is a must. Bonus if you’ve worked with Scala
  , Jinja-templated SQL
  , or DSL-based modeling frameworks like dbt/sqlmesh.
• Curiosity and systems thinking: You understand the tradeoffs between batch vs streaming, structured vs unstructured, cost vs latency-and you ask why before you build.
• Collaboration skills: You’ll work closely with ML engineers, platform architects, security teams, and domain data owners. Ability to communicate clearly and write clean, documented code is key.

What Makes This Role Special

• Impact at global scale: Your work will influence container journeys, terminal operations, vessel routing, and sustainability metrics across 130+ countries and $4T+ in global trade.
• Platform-level thinking: You’re not just solving one use case-you’re building primitives for others to reuse. This is your chance to shape a high-leverage internal data platform.
• Freedom to experiment: We don’t believe in checkbox engineering. You’ll have space to challenge the status quo, propose better tooling, and refine the foundations of our platform stack.
• Career-defining scope: Greenfield. Executive visibility.…