AWS Lakehouse Data Engineer

About this role

Job Family : Software Development & Support Travel Required : None Clearance Required : Ability to Obtain Public Trust

AWS Lakehouse Data Engineer We are seeking an AWS Lakehouse Data Engineer to design, implement, and operate the cloud-native data platform that powers AI/ML, analytics, reporting, and data visualization. You will build a modern lakehouse on Amazon S3 using AWS-native services and open table formats, providing Databricks-like capabilities while maintaining portability, strong governance, cost efficiency, and operational control. You will also develop scalable batch and streaming ingestion, Python and PySpark ETL/ELT pipelines, metadata and governance services, and automated cloud provisioning and CI/CD across environments.   This role is ideal for an engineer who enjoys platform building, automation, performance optimization, and enabling advanced analytics through trusted, secure, and well-governed data.  

What You Will Do     Build and Operate Data Pipelines (Batch and Streaming)   • Design and implement batch and streaming ingestion from APIs, relational databases, file drops, event streams, and external partners.  

• Implement, test, and optimize ETL/ELT pipelines using Python and PySpark to produce curated, analytics-ready datasets for reporting, visualization, and machine learning.  

• Implement incremental processing, change data capture (CDC), data contracts, schema validation, and reusable transformation frameworks.  

• Improve pipeline reliability through automated testing, orchestration, monitoring, retry handling, and operational runbooks.  

Deliver an AWS-Native Lakehouse Data Platform   • Design and implement a Delta Lakehouse-style data platform using AWS-native services to provide Databricks-like capabilities for data engineering, analysis, and data visualization.  

• Build and manage a scalable lakehouse on Amazon S3 using Apache Iceberg and open columnar formats such as Apache Parquet.  

• Implement SQL-like table reliability for data stored in Amazon S3, including ACID transactions, schema evolution, partition evolution, snapshot isolation, time travel, and rollback capabilities using Apache Iceberg.  

• Enable fast, interactive querying of lakehouse data using AWS-native query and compute services such as Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift where appropriate.  

• Optimize performance and cost through partitioning, compaction, file sizing, statistics, caching, lifecycle policies, and efficient separation of compute and storage.  

• Establish standardized development, test, and production environments with consistent configuration and controlled promotion across stages.  

Metadata, Governance, Access Control, Lineage, and Quality   • Implement data governance and fine-grained access control using AWS-native services, including AWS Lake Formation, AWS Glue Data Catalog, AWS Identity and Access Management (IAM), AWS Key Management Service (KMS), and related security services.  

• Implement a managed metadata repository for dataset cataloging, ownership, business definitions, tagging, classification, and discoverability.  

• Enable end-to-end lineage from source through transformation and consumption to support auditability, impact analysis, and regulatory requirements.  

• Apply policy-based access, least-privilege permissions, row-, column-, and cell-level controls where required, data classification, retention, encryption, and secure data handling.  

• Build operational data quality checks for freshness, completeness, uniqueness, validity, consistency, and anomaly detection, and publish measurable SLAs/SLOs.  

AWS Automation, CI/CD, and Operations   • Implement automated AWS provisioning using Infrastructure as Code (IaC) to create consistent environments and secure-by-default baselines.  

• Build and enhance CI/CD for data pipelines and lakehouse components, including automated tests, security checks, validation gates, packaging, deployment, promotion, and rollback strategies.  

• Implement observability with centralized metrics, logs, traces, alerts, dashboards, runbooks, and incident-response procedures.  

• Continuously evaluate platform performance, scalability, reliability, security, and cost, and implement measurable improvements.  

Cross-Team Collaboration and Documentation   • Work closely with data, application, analytics, AI/ML, security, networking, and cloud platform teams to support mission needs and delivery timelines.  

• Maintain high-quality engineering documentation, including architecture diagrams, data models, SOPs, interface specifications, operational runbooks, and secure configuration baselines.  

• Present technical findings, trade-offs, risks, and recommendations clearly to technical and non-technical stakeholders.  

What You Will Need   • Bachelor's degree in Engineering, Information Technology, Computer Science, Data Engineering, or a related field, or FOUR (4) years equivalent practical experience in leu of degree.  

• SIX (6) years of relevant experience.  

• Hands-on experience implementing AWS-native data lake or lakehouse architectures using Amazon S3 and services such as AWS Glue, Amazon Athena, Amazon EMR, AWS Lake Formation, and Amazon Redshift.  

• Strong experience developing production ETL/ELT pipelines using Python and PySpark, including data modeling, transformation, testing, performance tuning, and error handling.  

• Hands-on experience with Apache Iceberg, including ACID transactions, snapshots, schema and partition evolution, time travel, table maintenance, and query optimization.  

• Advanced SQL skills and experience supporting analytical queries, semantic layers, reporting tools, and data visualization workloads.  

• Experience implementing metadata management and governance capabilities, including cataloging, lineage, ownership, classification, policy enforcement, and fine-grained access controls.  

• Experience with AWS security fundamentals, including I

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