Data Engineer, MIDAS, Digital Acceleration
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Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazon's digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action
The Amazon Digital Acceleration (DA) org is looking for an analytical and technically skilled data engineer to join our team. In this role, you will play a critical part in developing foundational analytical datasets spanning orders, subscriptions, discovery, promotions, pricing and royalties. Our mission is to enable digital clients to easily innovate with data on behalf of customers and make product and customer decisions faster.
An ideal individual is someone who has deep data engineering skills around ETL, data modeling, database architecture and big data solutions. This individual should have strong business judgement, excellent written and verbal communication skills.
Key job responsibilities
1. Develop data products, infrastructure and data pipelines leveraging AWS services (such as Redshift, Kinesis, EMR, Lambda etc.) and internal BDT tools (Datanet, Cradle, QuickSight etc.
2. Improve existing solutions/build solutions to improve scale, quality, IMR efficiency, data availability, consistency & compliance.
3. Partner with Software Developers, Business Intelligence Engineers, MLEs, Scientists, and Product Managers to develop scalable and maintainable data pipelines on both structured and unstructured (text based) data.
4. Drive operational excellence strongly within the team and build automation and mechanisms to reduce operations
About the team
The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.
- 1+ years of data engineering experience
- Experience with SQL
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Job ID: 153938871
Skills:
Aws Lambda, AWS Glue, Data Warehousing, AWS Athena, Databricks, Sql, data lakes, AWS Step Functions, AWS EMR, Amazon QuickSight, distributed processing
Skills:
snowflake , S3, Pyspark, Pl Sql, Json, Apache Airflow, Lambda, Aws Cloud, Csv, Sparksql, Sql, Dataframes, RDD, Lake formation, Parquet, ELT pipeline development, Orchestration tools, Iceberg, Data ingestion, Glue, DBT Data Build Tool, Snowflake Tasks, Athena, Abinitio ETL tool, AWS Data Analytics Technology Stack
Skills:
Performance Tuning, Data Modeling, Query Optimization, Snowflake Data Warehouse, ETL processes, Data validation techniques, Database design principles, Cloud-based data storage and processing
Skills:
data engineering , Azure Databricks, Sql, Power Bi, Etl, Cosmos DB, Azure Synapse Analytics, Azure Data Factory, Python, Azure Data Lake, Delta Lake
Skills:
BigQuery, Performance Tuning, Schema Design, Data Modeling, Sql, Apache Airflow, Gcp, Docker, Python, AWS, Dimensional layered data modeling, ClickHouse, Data-quality frameworks, dbt, ETL/ELT, CI/CD
