AWS Lakehouse Data Engineer

Delan Associates, Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work

Job summary

Delan Associates, Inc. is seeking an AWS Lakehouse Data Engineer to design, implement, and operate a cloud-native data platform powering AI/ML, analytics, reporting, and data visualization. The position offers 100% remote work with preference for the DMV area.

You will build and operate batch and streaming data pipelines, implement Delta Lakehouse on S3 with Iceberg, and enable fast querying using Athena, EMR, Glue, and Redshift, while enforcing data governance and security at scale.

Qualifications

• 6+ years of relevant data engineering 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 IAM and least privilege, KMS encryption, secrets management, network security, logging, and secure SDLC practices.
• Experience provisioning AWS resources using IaC and operating data platforms across multiple environments.
• Experience building or operating CI/CD pipelines for data workflows, including testing, packaging, deployment automation, environment promotion, and rollback.

Responsibilities

• Build and operate data pipelines (batch and streaming).
• Implement, test, and optimize ETL/ELT pipelines using Python and PySpark to produce analytics-ready datasets.
• Implement incremental processing, CDC, data contracts, schema validation, and reusable transformations.
• Improve pipeline reliability through automated testing, orchestration, monitoring, retry handling, and runbooks.
• Deliver an AWS-native Lakehouse Data Platform.
• Design and implement a Delta Lakehouse-style platform on AWS for data science and visualization.
• Build and manage a scalable lakehouse on S3 using Iceberg and Parquet.
• Enable fast querying with AWS-native services such as Athena, EMR, Glue, and Redshift.
• Optimize performance and cost via partitioning, compaction, sizing, statistics, caching, lifecycle policies.
• Establish standardized dev/test/prod environments with controlled promotion across stages.
• Metadata, governance, access control, lineage, and quality.
• Implement a managed metadata repository for cataloging and discovery.
• Enable end-to-end lineage and auditability.
• Apply policy-based access and fine-grained controls; data classification and retention.

Skills

Python

PySpark

AWS

Delta Lake

Apache Iceberg

SQL

ETL/ELT

Data governance

CI/CD for data

IaC

Spark

Education

Bachelor's degree or equivalent practical experience

Tools

AWS Glue

Amazon Athena

Amazon EMR

AWS Lake Formation

Apache Airflow (MWAA)

Apache Iceberg

Docker

Terraform

GitHub Actions

Job description

Position: AWS Lakehouse Data Engineer Clearance: Ability to Obtain Public Trust Location: 100% Remote (prefer DMV) 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.

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 lieu 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 IAM and least privilege, KMS encryption, secrets management, network security, logging, and secure SDLC practices.
• Experience provisioning AWS resources using IaC and operating data platforms across multiple environments.
• Experience building or operating CI/CD pipelines for data workflows, including testing, packaging, deployment automation, environment promotion, and rollback.
• Ability to troubleshoot distributed data-processing workloads and optimize performance, reliability, and cost.

What Would Be Nice to Have

• Hands-on experience with Databricks, Delta Lake, or migrating Databricks workloads to AWS-native services and Apache Iceberg.
• Experience with AWS Step Functions, Amazon Managed Workflows for Apache Airflow (MWAA), Amazon Kinesis, AWS Database Migration Service (DMS), AWS Lambda, Amazon MSK, or similar ingestion and orchestration services.
• Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.
• Experience integrating lakehouse data with business intelligence and visualization tools such as Amazon QuickSight, Tableau, or Power BI.
• Experienced using AI-assisted coding tools, such as GitHub Copilot, ChatGPT, Cursor, or Kiro, to accelerate implementation while maintaining code quality, testing, review, privacy, and security controls.
• Knowledge graph and Graph RAG experience, including graph modeling, ontology and taxonomy alignment, entity resolution, relationship extraction, and hybrid retrieval that combines graph traversal with semantic or vector search.