Senior Data Engineer

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About the Role

Senior Data Engineer

Key Responsibilities

• Design, develop, and maintain

  ETL/ELT pipelines

  for large-scale structured and unstructured datasets.
• Architect and optimize

  data lakes, data warehouses, and lakehouse solutions

  on

  AWS, Azure, or GCP

  .
• Collaborate with data science teams to

  deploy and manage ML models

  in production using

  MLOps

  frameworks.
• Implement

  data validation, monitoring, and quality frameworks

  to ensure consistency and reliability.
• Build

  feature stores and real-time streaming pipelines

  for AI/ML workloads.
• Manage and automate

  data workflows and orchestration

  (Airflow, Databricks, Prefect).
• Work with business teams to translate data requirements into technical architecture.
• Integrate

  AI/ML models

  for data enrichment, predictive analytics, and process automation.
• Ensure compliance with data governance, security, and privacy frameworks.

Required Skills & Qualifications

• Bachelor’s/Master’s in Computer Science, Data Engineering, or related field.
• 7+ years of experience in

  data engineering and cloud-based data systems.

• Strong hands-on experience with

  Python, PySpark, and SQL.

• Deep understanding of

  AI/ML model deployment

  and automation workflows.
• Expertise with

  cloud data services:

• AWS (Glue, Redshift, S3, EMR, Lambda)
• Azure (Data Factory, Synapse)
• GCP (BigQuery, Dataflow, Vertex AI)
• Experience with

  MLOps tools

  – MLflow, SageMaker, Kubeflow, or Vertex AI.
• Familiarity with

  Airflow, Databricks, or Prefect

  for orchestration.
• Hands-on experience with

  CI/CD, Git, Docker, Terraform.

• Excellent analytical, communication, and problem-solving skills.

Good to Have

• Exposure to

  GenAI

  ,

  LLM fine-tuning

  , or

  vector databases (Pinecone, FAISS, ChromaDB)

  .
• Familiarity with

  data observability

  and

  lineage tools

  (Monte Carlo, Databand).
• Experience in

  Agile / DevOps

  environments and modern data mesh architecture.