Senior Data Engineer (AI/ML)

Neolatika; Maharashtra; On-site; INR 2,000,000 - 3,600,000; Full time

Job summary

Neolatika is seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines and architectures. The role focuses on both structured and unstructured data, enabling robust data platforms and seamless data delivery for AI/ML initiatives.

You will work with data scientists and ML engineers to operationalize models, ensure data quality, and manage ETL/ELT workflows across AWS/Azure/GCP environments. A strong emphasis on governance, security, and mentoring is required.

Qualifications

• Bachelor's degree in CS, Data Engineering, Information Systems, or related field.
• 6+ years of experience in data engineering or data platform development.
• Strong proficiency in Python, SQL, and distributed data processing frameworks.
• Experience with big data technologies such as Spark, Hadoop, or similar platforms.
• Hands-on experience with cloud platforms (AWS / Azure / GCP).
• Experience building ETL pipelines and data orchestration workflows.
• Understanding machine learning pipelines and model lifecycle.
• Experience with data warehousing solutions such as Snowflake, BigQuery, Redshift, or similar.
• Strong knowledge of data modeling, data governance, and data quality frameworks.

Responsibilities

• Design, develop, and maintain scalable data pipelines for structured and unstructured data.
• Build and optimize data lakes, data warehouses, and data platform architectures.
• Support AI/ML model development by preparing, transforming, and delivering high-quality datasets.
• Develop and manage ETL/ELT workflows using modern data engineering tools.
• Collaborate with data scientists and ML engineers to operationalize machine learning models.
• Implement data quality, validation, and monitoring frameworks.
• Optimize data processing performance and cost across cloud platforms.
• Develop and maintain data APIs and data services for downstream applications.
• Ensure data security, governance, and compliance standards are met.
• Mentor junior data engineers and contribute to best practices and architecture decisions.

Skills

Python

SQL

Distributed data processing

Education

Bachelor's in CS / Data Engineering / Information Systems

Tools

Spark

Hadoop

AWS

Azure

GCP

Snowflake

BigQuery

Redshift

Docker

Kubernetes

Job description

Key Responsibilities

• Design, develop, and maintain scalable data pipelines for structured and unstructured data.
• Build and optimize data lakes, data warehouses, and data platform architectures.
• Support AI/ML model development by preparing, transforming, and delivering high-quality datasets.
• Develop and manage ETL/ELT workflows using modern data engineering tools.
• Collaborate with data scientists and ML engineers to operationalize machine learning models.
• Implement data quality, validation, and monitoring frameworks.
• Optimize data processing performance and cost across cloud platforms.
• Develop and maintain data APIs and data services for downstream applications.
• Ensure data security, governance, and compliance standards are met.
• Mentor junior data engineers and contribute to best practices and architecture decisions.

Required Skills & Qualifications

• Bachelor's in computer science, Data Engineering, Information Systems, or related field.
• 6+ years of experience in data engineering or data platform development.
• Strong proficiency in Python, SQL, and distributed data processing frameworks.
• Experience with big data technologies such as Spark, Hadoop, or similar platforms.
• Hands-on experience with cloud platforms (AWS / Azure / GCP).
• Experience building ETL pipelines and data orchestration workflows.
• Understanding machine learning pipelines and model lifecycle.
• Experience with data warehousing solutions such as Snowflake, BigQuery, Redshift, or similar.
• Strong knowledge of data modeling, data governance, and data quality frameworks.

Preferred Skills & Key Competencies

• Experience with ML pipelines, feature stores, and model deployment frameworks.
• Familiarity with MLOps tools such as MLflow or Kubeflow.
• Experience with streaming technologies like Kafka or Spark Streaming.
• Knowledge of containerization and orchestration (Docker, Kubernetes).
• Experience working in Agile or DevOps environments.
• Strong problem-solving and analytical skills.
• Ability to work effectively with cross-functional teams.
• Strong communication, documentation, and mentoring skills.