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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in deploying and managing AWS data services, including MWAA, Redshift, and S3, while optimizing data architecture for performance and cost. Proficient in designing scalable data models and implementing CI/CD pipelines for data transformation and orchestration.
Highest-signal resume keywords
AWS Managed Workflows For Apache Airflow (MWAA)Amazon RedshiftDbtSQLTerraform
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Platform ArchitectureData Lake DesignDimensional ModelingColumnar File Formats (Parquet)Data TransformationEvent-Driven Data StreamingApache KafkaData Partitioning StrategiesACID TransactionsMetadata Management
Tools & Technologies
AWS GlueS3AWS CloudFormationApache IcebergDelta LakeApache Hudi
Industry Keywords
Data EngineeringCI/CD Deployment PipelinesOperational DashboardsData ObservabilityReal-Time Ingestion
Tech Stack
Tools & technologiesAirflowAmazon RedshiftApacheAWSKafkaPythonSQLTerraform
About the role
Key responsibilities & impact- Own the deployment, scaling, and orchestration of core data stack using AWS Managed Workflows for Apache Airflow (MWAA).
- Design and evolve the overall data platform architecture, making thoughtful tradeoffs around scalability, reliability, cost, and maintainability.
- Configure and optimize raw data storage in S3, managing Redshift external schemas (Spectrum) and AWS Glue Data Catalog.
- Define efficient partitioning strategies, leverage columnar file formats (Parquet), and optimize storage layouts for query performance, scalability, and cost.
- Design scalable analytical data models using dimensional modeling and lakehouse best practices to support reporting, analytics, and downstream consumers.
- Manage and optimize dbt to ensure efficient transformation, testing, and materialization of raw data within Amazon Redshift.
- Build observability into the data platform through monitoring, logging, alerting, and operational dashboards.
- Help design and implement the transition toward event-based ingestion into S3.
- Implement and maintain CI/CD deployment pipelines for dbt projects, Airflow DAGs, and infrastructure.
- Ensure high performance, access control, and uptime for BI tools connecting to Redshift.
Requirements
What you’ll need- Extensive hands-on experience deploying and managing AWS data services, specifically MWAA (Airflow), Redshift / Redshift Spectrum, S3, IAM, and Glue.
- Advanced proficiency with dbt (structuring dbt projects, configuring sources, writing custom macros, and optimizing incremental models).
- Solid hands-on experience deploying AWS data platform components using Terraform or AWS CloudFormation.
- Expert-level SQL skills, with a deep understanding of Redshift distribution/sort keys and optimizing queries across external schemas.
- Strong Python experience for developing Airflow DAGs, automation, integrations, and data engineering tooling
- Experience designing efficient data lakes using Parquet, partitioning strategies, metadata catalogs, and external table technologies such as Redshift Spectrum.
- Experience with open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi, including an understanding of ACID transactions, schema evolution, time travel, and metadata management.
- Experience with Apache Kafka or AWS MSK for event-driven data streaming and real-time ingestion into S3 (Bonus).
- Experience designing resilient streaming pipelines with appropriate delivery guarantees, reconciliation, backfill strategies, and schema contract management (Nice-to-Have).
Benefits
Comp & perks- Health insurance
- Flexible work arrangements
- Professional development opportunities