Senior AWS Data Engineer
Inizio Partners Corp · United States · posted today ago
Listing supplied by Himalayas. 101 Careers did not originate this post and applications are handled by the employer.
About this role
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:
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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.
Originally posted on Himalayas
