Senior Data Engineer
New
USFull-TimeSenior
Salary130,000 - 150,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLJavaCI/CDTerraformScalaData modelingCloudFormation
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (Master’s preferred).
- 5+ years of experience in data engineering, analytics engineering, or data platform development.
- Strong proficiency in SQL and at least one programming language such as Python, Java, or Scala.
- Hands-on experience building data pipelines, data models, and distributed data processing systems.
- Strong expertise with AWS services such as Lambda, Glue, Step Functions, S3, EMR, SNS/SQS, Lake Formation, and CloudWatch.
- Solid understanding of data architecture patterns including data lakes, lakehouses, and analytical warehouses.
- Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation) and CI/CD pipelines.
- Strong analytical and problem-solving skills with the ability to handle complex data challenges.
- Excellent communication skills to translate technical concepts for both technical and non-technical stakeholders.
Responsibilities
- Develop and maintain scalable batch and streaming data pipelines supporting analytics, reporting, and downstream applications.
- Build and optimize data ingestion, transformation, and orchestration workflows across structured and semi-structured data sources.
- Design and maintain curated, analytics-ready data models including dimensional, canonical, and domain-oriented datasets.
- Implement cloud-based data solutions aligned with enterprise architecture standards and security requirements.
- Ensure data reliability through performance tuning, observability, validation rules, and data quality controls.
- Support cloud data platform evolution including architecture improvements, tooling decisions, and modernization initiatives.
- Collaborate with analytics, data science, and reporting teams to enable self-service data consumption.
- Translate business requirements into scalable and reusable data structures and pipelines.
- Ensure compliance with governance, privacy, metadata, and data lineage standards.
- Promote engineering best practices including CI/CD, version control, testing, and documentation.
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