Data Engineer

Role Description
We are looking for an experienced Data Engineer to design, build, and optimize robust data pipelines and platforms on AWS. You will work closely with data analysts, data scientists, and product teams to
deliver reliable, scalable, and secure data solutions that power analytics and decision-making.
Designation: Data Engineer / Senior Data Engineer
Location: Noida / Gurgaon

Job Description:

• Key Responsibilities:
  Design, build, and maintain scalable ETL/ELT pipelines and data workflows on AWS
  Develop and optimize data models (OLTP/OLAP), schemas, and warehousing solutions (e.g.,
  Redshift, Snowflake, or similar)
  Build data ingestion frameworks for batch and streaming data from diverse sources (APIs,
  RDBMS, files, event streams)
  Implement data quality checks, validation rules, and monitoring for data reliability and SLAs
  Write efficient, production-grade Python and SQL for data transformations and orchestration
  Manage and optimize data storage, partitioning, and performance (query tuning, indexing, cost
  optimization)
  Implement orchestration and workflow management (e.g., Airflow/Step Functions)
  Collaborate with stakeholders to translate business requirements into technical specs and data
  contracts
  Ensure data security, governance, lineage, and compliance (IAM, encryption, PII handling)
  Set up observability for pipelines (logging, metrics, alerting) and support on-call rotations
  Contribute to CI/CD for data workloads, infrastructure-as-code, and best practices
  documentation

• Required Skills and Qualifications
  AWS: S3, Glue, Athena, Lambda, Step Functions, EMR or Glue Spark, Redshift, IAM,
  CloudWatch, CloudFormation/Terraform
  Programming: Python (pandas, PySpark), SQL (advanced)
  Orchestration: Apache Airflow (or MWAA), Step Functions
  Data Warehousing/Lakes: Redshift, Lake Formation, Parquet/Delta/Iceberg
  Streaming (nice-to-have): Kinesis, MSK (Kafka)
  Containers (nice-to-have): Docker, ECS/EKS
  
• Preferred:
  4 - 5 years of hands-on data engineering experience in production environments
  Strong expertise in AWS data services and cost/performance trade-offs
  Advanced SQL skills (window functions, CTEs, query tuning) and Python for data engineering
  Solid experience with ETL/ELT design patterns, data modeling (star/snowflake), and schema
  evolution
  Experience with Spark (Glue/EMR) or similar distributed processing frameworks
  Proficiency with workflow orchestration (Airflow or equivalent)
  Practical knowledge of data quality frameworks and automated testing for data pipelines
  Experience with version control (Git) and CI/CD for data jobs
  Strong understanding of security best practices (IAM roles/policies, encryption, VPC, Secrets
  Manager)