Senior Data Engineer (AI/ML)
Neolatika
- Location
- Pune Division, Maharashtra, India
- Job type
- Full-time
Required skills
- Python
- Agile
- AWS
- Azure
- big data technologies
- BigQuery
- compliance
- containerization
- cross-functional
- data modeling
- DevOps
- Docker
- ETL
- GCP
- Hadoop
- Kafka
- Kubeflow
- Kubernetes
- machine learning
- Snowflake
- Spark
- SQL
About the role
Website:
neolatika.com
Job details:
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.
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