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AWS Lakehouse Data Engineer at Guidehouse Inc. · Katori

Asks for

The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Hands-on experience with Apache Iceberg, including ACID transactions, snapshots, schema and partition evolution, time travel, table maintenance, and query optimization.”
The ad says“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.”
The ad says“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.”
The ad says“Experience building or operating CI/CD pipelines for data workflows, including testing, packaging, deployment automation, environment promotion, and rollback.”
The ad says“Implement, test, and optimize ETL/ELT pipelines using Python and PySpark to produce curated, analytics-ready datasets for reporting, visualization, and machine learning.”
The ad says“Experience provisioning AWS resources using IaC and operating data platforms across multiple environments.”
The ad says“Experience with AWS security fundamentals, including IAM and least privilege, KMS encryption, secrets management, network security, logging, and secure SDLC practices.”
The ad says“Experience with AWS security fundamentals, including IAM and least privilege, KMS encryption, secrets management, network security, logging, and secure SDLC practices.”
The ad says“Implement, test, and optimize ETL/ELT pipelines using Python and PySpark to produce curated, analytics-ready datasets for reporting, visualization, and machine learning.”
The ad says“Implement, test, and optimize ETL/ELT pipelines using Python and PySpark to produce curated, analytics-ready datasets for reporting, visualization, and machine learning.”
The ad says“Advanced SQL skills and experience supporting analytical queries, semantic layers, reporting tools, and data visualization workloads.”

Values

The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Experience integrating lakehouse data with business intelligence and visualization tools such as Amazon QuickSight, Tableau, or Power BI.”
The ad says“Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.”
The ad says“Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.”
The ad says“Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Experience 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.”
The ad says“Experience 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.”
The ad says“Hands-on experience with Databricks, Delta Lake, or migrating Databricks workloads to AWS-native services and Apache Iceberg.”
The ad says“Hands-on experience with Databricks, Delta Lake, or migrating Databricks workloads to AWS-native services and Apache Iceberg.”
The ad says“Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.”
The ad says“Experience 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.”
The ad says“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.”
The ad says“Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.”
The ad says“Experience 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.”
The ad says“Experience integrating lakehouse data with business intelligence and visualization tools such as Amazon QuickSight, Tableau, or Power BI.”
The ad says“Experience integrating lakehouse data with business intelligence and visualization tools such as Amazon QuickSight, Tableau, or Power BI.”
The ad says“Experience with modern DevOps practices and tools such as Git, Terraform, AWS CloudFormation or AWS CDK, Jenkins, AWS CodePipeline, GitHub Actions, and Docker.”

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 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.

Experience 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.

The annual salary range for this position is $113,000.00-$188,000.00. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs.

What We Offer:

Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.

Benefits include:

Medical, Rx, Dental & Vision Insurance
Personal and Family Sick Time & Company Paid Holidays
Parental Leave
401(k) Retirement Plan
Group Term Life and Travel Assistance
Voluntary Life and AD&D Insurance
Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
Transit and Parking Commuter Benefits
Short-Term & Long-Term Disability
Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
Employee Referral Program
Corporate Sponsored Events & Community Outreach
Care.com annual membership
Employee Assistance Program
Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
Position may be eligible for a discretionary variable incentive bonus

About Guidehouse

Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.

Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.

If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1-571-633-1711 or via email at RecruitingAccommodation@guidehouse.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.

All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or guidehouse@myworkday.com. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.

If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact recruiting@guidehouse.com. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.

Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.