About this role
Associate Technical Architect - Data Engineering focusing on building scalable AWS data lakehouse solutions with semantic modeling and governance. Leads design of large data platforms and real-time pipelines across analytics ecosystems.
Key Responsibilities
- Design and implement data warehouses/lakes on AWS
- Lead data pipelines including real-time with Kinesis
- Build semantic models and governance
- Collaborate on BI and analytics tooling
- Mentor distributed teams
Technical Overview
Expertise in AWS data services (Glue, Lambda, EMR, Kinesis, MWAA, Lake Formation); Redshift with Spectrum/Serverless; Athena; RDS; PySpark; SQL; NoSQL (DynamoDB, MongoDB, DocumentDB); BI tooling; data governance and data mesh concepts.
Ideal Candidate
The ideal candidate is a senior data engineer with 5+ years designing data warehouses/lakes on AWS, deep experience with semantic layers (AtScale), and hands-on PySpark/Python to build real-time and batch pipelines in a modern data stack.
Must-Have Skills
5+ years of designing and implementing data warehouses and data lakes/lakehouses on AWSHands-on experience with AtScale or similar semantic layer toolsBuilding large-scale data lake architectures on Amazon S3Governance and cataloging with AWS Lake FormationETL and metadata frameworks using AWS GlueServerless data processing with AWS LambdaDistributed data workloads on Amazon EMRReal-time data pipelines with AWS KinesisOrchestrating pipelines with AWS Step Functions/Amazon MWAASchema design for Amazon Redshift and Spectrum/ServerlessInteractive querying with Amazon AthenaOperational databases with Amazon RDS (PostgreSQL/MySQL/Aurora)Data migration using AWS DMS and Glue ConnectorsSemantic modeling and BI tool integration (QuickSight/Tableau/Power BI)Python and PySpark
Nice-to-Have Skills
FSI/Retail/CPG domain experienceTerraform / IaCData virtualization (QuickSight/Tableau/Power BI) and CollibraAWS IAMSecrets ManagerCloudWatch/CloudTrail/KMSGenAI tools (GitHub Copilot) and AI-assisted developmentData Mesh experience
Tools & Platforms
Amazon Web ServicesAtScaleAWS Lake FormationAWS GlueAWS LambdaAmazon EMRAmazon KinesisAmazon MWAAAmazon RedshiftRedshift SpectrumRedshift ServerlessAmazon AthenaAmazon RDSPostgreSQLMySQLAuroraDMSGlue ConnectorsEventBridgeSNSSQSQuickSightTableauPower BIPySparkPythonDynamoDBMongoDBDocumentDBTerraform
Required Skills
AWSAmazon Web Servicesdata warehousesdata lakesdata lakehousesAtScalesemantic layerPythonPySparkAWS GlueAWS LambdaAmazon EMRAWS KinesisAWS Step FunctionsAmazon MWAAAmazon RedshiftRedshift SpectrumRedshift ServerlessAmazon AthenaAmazon RDSPostgreSQLMySQLAuroraDMSGlue ConnectorsEventBridgeSNSSQSdata governancesemantic modelingQuickSightTableauPower BIDynamoDBMongoDBDocumentDB
Hard Skills
AWSAmazon Web ServicesAtScalesemantic layerPythonPySparkAWS GlueAWS LambdaAmazon EMRAWS KinesisAWS Step FunctionsAmazon MWAAAmazon RedshiftAthenaRDSPostgreSQLMySQLAuroraDMSGlue ConnectorsEventBridgeSNSSQSdata governancesemantic modelingBI toolsQuickSightTableauPower BIDynamoDBMongoDBDocumentDBPythonPySpark
Soft Skills
communicationcollaborationteamworkproblem-solvingplanningpresentation
Keywords for Your Resume
associate architectdata engineeringdata warehousesdata lakesdata lakehousesatscalesemantic layerawsamazon web servicesaws lake formationaws glueaws lambdaamazon emraws kinesisaws step functionsamazon mwaaamazon redshiftredshift spectrumredshift serverlessamazon athenaamazon rdspostgresqlmysqlauroradmsglue connectorseventbridgesnssqsdata governancesemantic modelingquickSighttableaupower bidynamo dbmongodbdocumentdbdata meshAWSAmazon Web ServicesAtScalePythonPySparkAWS GlueAWS LambdaAmazon EMRAWS Kinesis
Deal Breakers
Lack of 5+ years data warehouse / data lake experience on AWS, No experience with semantic layers or AtScale, No hands-on Python or PySpark
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