Associate Architect - Data Engineering
Remote (USA/Canada)Full-TimeMiddle
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years
- Required Skills
- AWSPostgreSQLPythonSQLAmazon RDSMicrosoft Power BIMongoDBMySQLTableauAWS LambdaPySpark
Requirements
- 5+ years of experience in designing and implementing data warehouses and data lakes/lakehouses on AWS
- Hands-on experience with AtScale or similar semantic layer tools
- Proven success working with globally distributed teams
- Deep working knowledge across key AWS Data & Analytics services
- Building large-scale data lake architectures on Amazon S3 and open table formats
- Implementing governance and cataloging through AWS Lake Formation
- Developing ETL and metadata frameworks using AWS Glue
- Leveraging AWS Lambda for serverless data processing
- Running distributed data workloads on Amazon EMR
- Enabling real-time data pipelines with AWS Kinesis (Data Streams and Firehose)
- Orchestrating pipelines using AWS Step Functions/Amazon MWAA/similar services
- Designing and optimizing schemas and query performance on Amazon Redshift, including Spectrum and Serverless features
- Querying large datasets interactively using Amazon Athena
- Managing operational databases using Amazon RDS across engines such as PostgreSQL, MySQL, and Aurora
- Integrating and migrating data using AWS DMS, Glue Connectors, EventBridge, SNS, and SQS
- Strong understanding of semantic modeling, including logical data models, virtual cubes, and centralized metric definitions
- Experience optimizing performance using query pushdown, caching, and aggregate awareness over platforms like Redshift and Athena
- Ability to integrate semantic layers with BI tools (QuickSight, Tableau, Power BI) and enforce row/column-level security
- Strong programming capability in Python and PySpark for large-scale data processing
- Proficiency in writing complex SQL queries, analytical functions, and performance tuning for large datasets
- Familiarity with NoSQL databases such as Amazon DynamoDB, MongoDB, or DocumentDB
- Strong understanding of partitioning, indexing, scaling approaches, and query optimization techniques
- Proven experience in architecting and implementing data pipelines using native AWS services
- Solid understanding of data modeling concepts, including dimensional, normalized, and lakehouse patterns
View Full Description & ApplyYou'll be redirected to the employer's site
