Data Engineer

Role :- Data Engineer

Experience:

Role Overview

Data Engineer with deep AWS expertise

You will design, build, and maintain scalable data pipelines, implement secure cloud data architectures, optimize storage and compute performance, and ensure end-to-end operational reliability of our SaaS platform.

AI, vector search, or modern data processing techniques

Key Responsibilities:-

AWS-Centric Data Engineering

• Design and implement

  ETL/ELT pipelines

  using AWS Glue, EMR, Lambda, and Step Functions
• Develop optimized

  Athena queries, partitions, data catalog management

  , and metadata workflows
• Manage and optimize

  Amazon S3 data lakes

  (performance tuning, lifecycle policies, cost optimization)
• Set up and maintain

  PostgreSQL/RDS

  for operational and analytical workloads
• Implement

  VPC, subnets, security groups, NAT gateways, and private networking

  for secure data operations

Data Pipeline & Platform Operations

• Build highly scalable batch and real-time data workflows
• Develop connectors for diverse data sources (databases, cloud storage, SaaS APIs)
• Implement data quality validation, monitoring, and alerting across pipelines
• Ensure high availability, reliability, and performance of data systems for our SaaS platform
• Set up

  CI/CD pipelines

  , infrastructure automation using

  Terraform

  , and containerized workflows using

  Docker/Kubernetes

AI & Advanced Processing (Good to Have)

• Basic knowledge of embeddings, tokenization, and vector DBs (OpenSearch, Pinecone, Weaviate, Chroma)
• Experience with document processing, OCR, semantic chunking, or RAG pipelines
• Integration exposure with LLM services (OpenAI, Bedrock, Databricks)

Required Technical Skills

• AWS Services (Primary Focus):

  EMR, Glue, Athena, S3, RDS/Postgres, Lambda, IAM, VPC, CloudWatch

• Data Engineering:

  Python (pandas, NumPy), SQL, ETL/ELT design, data warehousing, data modeling

• Distributed Processing:

  Spark on EMR (mandatory), Ray (optional)

• Streaming Systems:

  Kafka or Kinesis (good to have)

• DevOps & Infra:

  Docker, Kubernetes, Terraform, GitHub Actions, CI/CD pipelines

• AI/Data Tools (Advantage):

  Vector databases, MLflow/DVC, embedding generation, RAG pipelines

Required Experience

• 3-5 years of experience as a Data Engineer
• Strong hands-on experience with

  AWS analytics & data services

• Experience managing and optimizing

  S3-based data lakes

• Strong experience with

  PostgreSQL/RDS

• Prior work on secure cloud architectures (IAM, VPC, subnets)
• Experience with ETL/ELT pipeline design for high-volume workloads
• Exposure to SaaS platform operations or multi-tenant architectures is a plus

Key Skills for Success

• Strong analytical and problem-solving mindset
• Deep understanding of AWS data ecosystem
• Ability to work in a fast-paced startup environment
• Ownership mentality and willingness to build from the ground up
• Collaborative approach with cross-functional teams

What We Offer

• Work on cutting-edge Gen AI products solving real-world problems
• Collaborate with AWS on innovative AI initiatives
• Competitive salary + equity in growing AI company
• Professional growth in rapidly evolving AI landscape
• Flexible, innovative culture valuing creativity and ownership