Software Engineer (Java + Azure Data Engineering)

Job Description

Software Engineer (Java + Azure Data Engineering) – Expert Polyglot and AI‑Driven Developer

Overview

A highly experienced engineer with 5+ years of deep programming expertise who thrives on solving complex problems independently and in teams. Primarily skilled in Java 8+, complemented by strong proficiency in Azure data engineering (ADF, Databricks, ADLS, PySpark) and Vue.js for front-end development. Comfortable navigating multiple programming paradigms and equipped to work on full‑stack, cloud‑native, and data‑intensive development at pace and quality.

Key Responsibilities

• Backend & APIs (Java): Design and develop scalable microservices and APIs using Java (Spring Boot) with maintainable and secure code.
• Front‑end: Create responsive, modular front-end applications with Vue.js (or similar), following sound UI/UX and component design.
• Cloud DevOps: Deploy and manage cloud infrastructure on Azure, leveraging DevOps pipelines, containers (Docker/Kubernetes), and infrastructure as code.
• Data Engineering – Batch & Orchestration:
  • Build and operate Azure Data Factory (ADF) pipelines (triggers, activities, mapping data flows) for ingestion from APIs, databases, and files.
  • Develop scalable data transformations in Azure Databricks using PySpark and Delta Live Tables (DLT) for reliable, declarative pipelines.
• API Data Consumption: Design and implement robust ingestion frameworks to consume data from REST APIs, handle pagination, authentication, and error recovery for large-scale data loads.
• Data Lake & Storage:
  • Implement Medallion Architecture (Bronze → Silver → Gold) on ADLS Gen2 with proper folder hierarchy, ACLs, governance, and cost optimization.
  • Integrate Azure Blob Storage and Azure SQL Database for curated and serving layers.
• Real‑Time & Streaming: Design and maintain streaming pipelines using Azure Kafka integration (e.g., Azure Event Hubs with Kafka protocol) and Spark Structured Streaming for low-latency data products.
• Performance Engineering :
  • Optimize PySpark jobs for partitioning, caching, shuffle reduction, and broadcast joins.
  • Tune ADF pipelines for efficient data movement and concurrency.
  • Apply SQL query optimization techniques (indexes, joins, window functions) for faster retrieval.
  • Design and optimize database views for downstream analytics and BI consumption.
• Data Modeling & Performance: Design schemas (star/snowflake), optimize queries (SQL window functions, CTEs), and ensure efficient retrieval for downstream analytics.
• BI & Analytics Enablement:
  • Prepare analytics-ready datasets and semantic models for visualization tools like Power BI and Apache Superset; ensure proper data contracts for downstream consumers.
  • Partner with web teams to implement Google Tag Manager (GTM) tagging and telemetry that power Google Analytics and downstream analytics.
• Collaboration & Leadership: Collaborate globally with product owners, architects, data scientists, and developers; mentor peers on clean code, data best practices, and continuous learning. Own commitments and deliver high-quality software within deadlines.

Essential Skills and Tools

• Java (8+): In-depth knowledge of core Java, concurrency, JVM internals, and functional programming paradigms.
• Frameworks: Expertise in Spring Boot, Spring Security, Hibernate, and reactive frameworks (WebFlux).
• Frontend: Strong skills in Vue.js, modern JavaScript/TypeScript, and CSS preprocessors.

Data Engineering

Programming & Scripting: Java or Python (data ingestion, transformation), SQL (advanced joins, window functions, CTEs), PySpark (large-scale transformations).

• Azure Data Services:
  • Azure Data Factory (ADF): Pipelines, triggers, activities, Mapping Data Flows, CI/CD with Azure DevOps.
  • Azure Databricks: Notebooks (PySpark), Delta Lake, Delta Live Tables, job clusters, Unity Catalog (governance).
  • Storage: ADLS Gen2 (hierarchy, ACLs), Azure Blob, Azure SQL Database.
• Real‑Time Processing: Azure Kafka integration (e.g., Event Hubs Kafka endpoint), Spark Structured Streaming.
• Data Warehousing & Modeling: Medallion Architecture (Bronze/Silver/Gold), dimensional modeling (star/snowflake), surrogate keys, CDC patterns.
• Visualization Tools: Power BI, Apache Superset, and integration with downstream analytics platforms.
• Web Analytics: Google Tag Manager (GTM) for event tagging; collaboration on Google Analytics instrumentation and taxonomy.

Performance Engineering

• PySpark optimization (partitioning, caching, shuffle reduction).
• ADF pipeline tuning (parallelism, concurrency).
• SQL query optimization (indexes, joins, views).
• Efficient view design for BI and reporting.

Platform & Engineering Excellence

• Cloud Platforms: Proficient with Microsoft Azure services including App Services, AKS, Azure DevOps.
• Build & Dependency: Maven, Gradle with effective repository management.
• Version Control: Strong knowledge of Git (branching strategies, pull requests, merge workflows).
• CI/CD: Jenkins/GitHub Actions/Azure Pipelines.
• Containers & Orchestration: Docker, Kubernetes (AKS), Helm.
• Database Technologies: PostgreSQL, MongoDB, Azure SQL Database; schema design, indexing, partitioning, and query optimization.
• Collaboration & Agile: Experience in distributed teams using Scrum/Agile; excellent verbal and written communication.

Attributes

• Self‑disciplined and reliable, delivering commitments on schedule with excellence.
• Fast learner with a passion for new technologies and data performance optimization.
• Strong problem solver who values code quality, data reliability, and maintainability.
• Team player willing to take on challenges and mentor others across application and data disciplines.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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