Senior AWS Data Engineer

Role Overview:

• Lead the design, development, and optimization of large-scale, reliable, and secure data pipelines and data lake architecture on AWS.
• Architect and implement end-to-end data solutions, including data ingestion, storage, transformation, and analytics using AWS services (Glue, Redshift, S3, Lambda, EMR, Kinesis, Athena, RDS, etc.).
• Mentor and guide a team of data engineers, conducting code reviews and fostering best practices in data engineering and cloud architecture.
• Collaborate with data scientists, analysts, and business stakeholders to translate requirements into scalable and maintainable solutions.
• Oversee migration of data from legacy systems to AWS-based data lakes and data warehouses.
• Develop and enforce standards for data quality, security, and governance.
• Drive the adoption of DevOps, CI/CD, and infrastructure-as-code practices within the data engineering team.
• Ensure solutions are cost-effective, performant, and aligned with enterprise data strategy.
• Stay current with advancements in AWS technologies and data engineering trends and evaluate new tools and frameworks for potential adoption.
• Troubleshoot complex data issues and provide technical leadership in problem resolution.

Key Responsibilities & Skillsets:

• Common Skillsets:
  • Superior analytical and problem solving skills
  • Should be able to work on a problem independently and prepare client ready deliverable with minimal or no supervision
  • Good communication skill for client interaction

Application development Skillsets:

• Strong ability to debug complex data workflows, optimize application and ETL code, and automate data transformation processes
• Systematic, analytical problem‑solving approach with strong ownership over data quality, performance, and delivery
• Ability to quickly evaluate new AWS data and analytics services and determine fit for data pipelines or application architecture
• Hands‑on experience developing data workflows and infrastructure using IaC frameworks such as CloudFormation or Terraform (as needed)
• Working knowledge of CI/CD pipelines primarily to support data application deployments (Jenkins, CodePipeline, etc.)
• Proficient with Git for versioning data processing code, libraries, and application components
• Skilled in writing production‑grade code in Python, Bash, PowerShell, or similar languages, focusing on data processing and backend development
• Experience using Docker for packaging applications and data-processing workloads, with exposure to containerized data services (ECS, EKS, etc.)
• Comfortable developing and troubleshooting in Linux environments
• Solid understanding of key AWS data services and application primitives such as S3, EC2, Glue, EMR, Lambda, RDS, DynamoDB, CloudWatch, and VPC networking concepts
• Strong knowledge of AWS security and IAM as it relates to data pipelines, encryption (KMS), secure data access (IAM roles/policies), and audit controls
• Hands‑on experience with ETL, distributed compute, and big data frameworks such as Spark, Glue, Hadoop/EMR, Impala, or similar tooling
• Deep understanding of relational databases, SQL optimization, and application‑to‑database interaction patterns
• Familiarity with log analytics and observability platforms (Splunk, ELK, Prometheus, Grafana) as they relate to monitoring data pipelines and applications

Candidate Profile:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in data engineering, with at least 3 years in technical leadership or lead engineer role.
• Extensive hands-on experience with AWS data services (Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, Athena, RDS, API Gateway, etc.).
• Proficient in programming languages such as Python and SQL; experience with Shell scripting and Scala is a plus.
• Strong experience designing, implementing, and managing data lakes, data warehouses, and data ingestion pipelines on AWS.
• Proven experience with ETL/ELT processes, data modeling, and big data frameworks.
• Demonstrated ability to lead, mentor, and coach engineers in a collaborative team environment.
• Experience with DevOps practices, CI/CD pipelines, and infrastructure-as-code tools (e.g., CloudFormation, Terraform).
• Excellent problem-solving, communication, and organizational skills.