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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