Remote AWS Lakehouse Data Engineer

Delan Associates, Inc; Atlanta (GA); Remote; USD 120,000 - 180,000; Full time

Job summary

Delan Associates, Inc. is seeking an AWS Lakehouse Data Engineer to design and operate a cloud-native data platform powering AI/ML, analytics, reporting, and data visualization.

You will build a modern lakehouse on Amazon S3 using AWS-native services and open table formats, ensuring portability, governance, cost efficiency, and operational control. This role emphasizes scalable batch and streaming ingestion, Python and PySpark ETL/ELT pipelines, metadata and governance services, and automated

Qualifications

• Bachelor's degree in Engineering, IT, CS, Data Engineering, or 4+ years equivalent practical experience.
• 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.

Responsibilities

• 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.

Skills

Python

PySpark

SQL

ETL/ELT

Apache Iceberg

AWS

Data governance

IAM/KMS security

IaC

CI/CD

Spark

Education

Bachelor's degree in a related field

Tools

Databricks

Delta Lake

Terraform

CloudFormation

CDK

Jenkins

GitHub Actions

Docker

Job description

Delan Associates, Inc. is seeking an AWS Lakehouse Data Engineer to design and operate a cloud-native data platform powering AI/ML, analytics, reporting, and data visualization.

You will build a modern lakehouse on Amazon S3 using AWS-native services and open table formats, ensuring portability, governance, cost efficiency, and operational control. This role emphasizes scalable batch and streaming ingestion, Python and PySpark ETL/ELT pipelines, metadata and governance services, and automated