Data Engineer

APM Terminals

3 - 6 years

6 - 9 Lacs

bengaluru

Posted:4 days ago| Platform:

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Skills Required

continuous integration cd cloud services python scala amazon redshift ci/cd microsoft azure data engineering apache flink azure devops sql docker coding testing frameworks java orchestration framework data modeling spark gcp kafka bigquery aws etl

Work Mode

Work from Office

Job Type

Full Time

Job Description

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).

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