Maersk Recruitment 2026 hiring Associate Data Engineer Job, Pune

Apply for Maersk Recruitment 2026! Hiring Associate Data Engineer Job in Pune for ETL pipelines, cloud data platforms (AWS/Azure/GCP), and ML Ops. Open for BE/BTech/BSc/ME/MTech freshers and experienced candidates

Candidates who are interested in Maersk Recruitment Job Openings can go through the below to get more information.

Key Job details of Associate Data Engineer job

Company: Maersk

Qualifications: BE/BTech/BSc/ME/MTech/MSc

Experience Needed: Freshers/Experienced

Job Req ID: R163935

Location: Pune

Job Description

We are seeking a highly skilled Data Engineer with deep expertise in data engineering concepts, cloud platforms, engineering and operational excellence, and ML Ops practices. The candidate will play a key role in designing, building, and optimizing scalable data infrastructure, enabling efficient data flows across the organization, and supporting machine learning initiatives.

Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes to process structured and unstructured data.
• Implement robust data models, data lakes, and data warehouses that enable analytics and machine learning use cases.
• Collaborate with software engineers, data scientists, and business stakeholders to deliver high-quality, production-ready data solutions.
• Ensure data quality, governance, and lineage tracking across all data assets.
• Drive operational excellence through automation, monitoring, and alerting for data systems.
• Apply engineering best practices (CI/CD, testing frameworks, code reviews, performance optimization) to ensure reliability and maintainability of data workflows.
• Partner with ML engineering and research teams to establish ML Ops pipelines, ensuring reproducibility, model deployment, monitoring, and scalability in production environments.
• Optimize cloud-based data platforms (e.g., AWS, Azure, GCP) for cost, performance, and security.
• Contribute to internal standards, documentation, and guidelines for data engineering excellence.

Required Skills and Qualifications

• Strong foundation in data engineering concepts: data modeling, pipelines, batch/streaming (Kafka, Spark, Flink, or similar).
• Proficiency in cloud services (AWS, Azure, or GCP) with solid experience in cloud-native data tools (e.g., BigQuery, Redshift, Synapse, Databricks, Snowflake).
• Hands-on expertise with modern data orchestration frameworks (Airflow, Prefect, Dagster).
• Strong coding skills in Python, SQL, and one additional language (Scala/Java preferred).
• Experience with engineering excellence practices: version control, CI/CD, unit/integration testing, observability, and performance optimization.
• Background in operational excellence methodologies (SRE principles, system reliability, monitoring, alerting).
• Familiarity with ML Ops frameworks (MLflow, Kubeflow, Vertex AI, or Azure ML) and ability to work closely with ML engineers.
• Understanding of containerization and orchestration (Docker, Kubernetes).
• Knowledge of data governance and compliance best practices (security, access management, GDPR/PII handling).

Preferred Qualifications

• Experience in designing large-scale data platforms serving both analytics and AI/ML needs.
• Exposure to real-time streaming architectures.
• Familiarity with DevOps principles in the context of data and machine learning workflows.
• Strong problem-solving skills, with an emphasis on scalability and reliability.
• Excellent communication skills and ability to work in cross-functional, global teams.

Education

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

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Interview Questions

How do you design ETL/ELT pipelines for batch and streaming data using Spark or Flink?
Explain data modeling for data lakes vs warehouses (e.g., Snowflake, Databricks).
What orchestration tools like Airflow have you used for workflow management?
Describe implementing ML Ops pipelines with MLflow or Kubeflow.
How do you ensure data quality, governance, and lineage in cloud environments?
What CI/CD practices apply to data engineering (version control, testing)?
Explain containerization with Docker/Kubernetes for data workloads.
How do you optimize costs and performance in AWS S3, Redshift, or BigQuery?
Describe monitoring/alerting for data pipelines using SRE principles.
How have you collaborated with data scientists on production ML deployments?

Selection Process

Online application via Maersk Careers (MyWorkday).
Technical assessment on SQL/Python, data modeling, and cloud tools.
Technical interview(s) on pipelines, ML Ops, and system design.
Final behavioral round on communication and cross-functional collaboration.

Pro Tip

Build a sample data pipeline with Airflow, Spark, and Docker deployed to AWS/GCP, including basic ML model integration, to demonstrate hands-on expertise.
Emphasize cloud-native tools (Databricks, Snowflake) and operational excellence like monitoring/SRE, as Maersk values scalability and reliability.