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 data.
• Build and optimize

  data models, data lakes, and warehouses

  on cloud platforms (AWS/Azure/GCP).
• Partner with

  data scientists

  to operationalize ML models and automate model lifecycle workflows (

  MLOps

  ).
• Implement

  data validation, quality, and monitoring frameworks

  to ensure data reliability.
• Develop

  feature stores

  and

  real-time streaming solutions

  for AI/ML applications.
• Collaborate with

  business and product teams

  to translate data requirements into scalable engineering solutions.
• Leverage

  AI/ML techniques

  to enhance predictive analytics, data enrichment, and process automation.
• Manage and optimize

  data orchestration workflows

  using Airflow, Databricks, or Prefect.
• Ensure compliance with

  data governance, privacy, and security

  best practices.

Required Skills & Qualifications:

• Bachelor’s/Master’s degree in

  Computer Science, Data Engineering

  , or a related field.

• 7+ years

  of experience in

  data engineering

  with proven expertise in data pipeline and data lake design.
• Strong proficiency in

  Python, SQL, and PySpark

  for data processing and ML integration.
• Hands-on experience with

  cloud data services

  :

• AWS:

  Glue, Redshift, S3, EMR, Lambda

• Azure:

  Data Factory, Synapse

• GCP:

  BigQuery, Dataflow
• Solid understanding of the

  AI/ML lifecycle

  , including model training, validation, and deployment.
• Experience with

  MLOps tools

  such as MLflow, SageMaker, Kubeflow, or Vertex AI.
• Proficiency in

  data orchestration (Airflow, Databricks Workflows)

  and

  CI/CD pipelines

  for data systems.
• Familiarity with

  Git

  ,

  Docker

  , and

  Terraform

  for version control, containerization, and infrastructure automation.
• Strong

  analytical thinking, problem-solving

  , and

  communication

  skills.

Good to Have:

• Experience with

  Generative AI (GenAI)

  ,

  LLM fine-tuning

  , or

  vector databases

  (Pinecone, FAISS, ChromaDB).
• Exposure to

  data observability and lineage tools

  (Monte Carlo, Databand, etc.).
• Prior experience working in

  Agile

  and

  DevOps

  environments.