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Pure Talent Consulting · 1 day ago

AWS Data Engineer

United States

Contract

Remote

Senior Level

5+ years exp

Pure Talent Consulting is seeking an AWS Data Engineer responsible for designing, developing, and implementing a reporting platform and architecture. The role focuses on Business Intelligence and Data Analytics, ensuring timely data solutions for stakeholders while collaborating with various teams.

Responsibilities

Can design, extend, and document a modern ELT lakehouse for all company data

S3 parquet medallion layers, Glue Catalog, clean architecture/docs, flows, modeling artifacts

AWS-first engineering — deep platform skills, not AWS console work

Glue (PySpark), DMS CDC, AppFlow, Step Functions, Lambda, IAM, CDK

Strong with NAWS CI/CD IaC — can build, extend, and debug pipelines and CDK Stacks in single or multi account deployments

CodeCommit, CodePipeline, CodeBuild etc

Owns source system changes and cross-system integration end-to-end

Schema alignment, SCD2, filling gaps between systems with no shared keys, building composite business keys, entity resolution, and shaping normalized/dimensional Gold models (dim/fact) and denormalized reporting layer (rpt)

A developer with a proper dev workflow

WSL/Linux, Docker Glue containers, pytest/Spark testing, mypy, ruff, Git flow, CI/CD pipelines

Understands data governance & security patterns and can help harden the platform

Lake Formation permissions, table/column-level controls, catalog organization, cross-account access patterns

Can design normalized → denormalized layers in Gold for BI, apps, and reporting

Entity modeling, dimensional design, surrogate keys, star schemas, wide reporting views

Forward-looking engineer who can grow the platform into streaming + AI

Kinesis/MSK ingestion, Delta/Iceberg, ML-driven enrichment, scalable gold models

Data Warehouse/Data Lake Architecture and Development

Data Modeling & Architecture

Design and maintain the architecture in reporting platforms, including the QuickSight

Synthesize many disparate data sources into one centralized data platform

Lead efforts to design, develop and implement database enhancement to improve efficiency and streamline the use for analytics, business analysis

Scope out and develop data enhancement plans for company initiatives

Implement and enforce data validation procedures

Make suggestions for improvements to the data team and team processes

Produce complete and accurate reports to business entities and users

Other duties as assigned

Qualification

AWS data toolsData Warehouse architectureData ModelingPythonSQL ServerETL/ELT PipelinesAPI architecturesSelf-directedCollaborationProblem solving

Required

Bachelor's degree required in Data Science, Software Engineering, Information Technology, or a related field

Minimum 5 years hands experience with AWS Data Tools

2+ years of experience as a technical lead on data and analytics projects

5+ years of experience architecting and maintaining enterprise data platforms

Expert level in AWS data tools (QuickSight)

Expert level user of Microsoft SQL Server tools and SQL

Expert level working knowledge of The Python language

Experience validating databases for accuracy

Experience working with API and event-driven architectures

Experience working with ETL/ELT Pipelines

Knowledge of how data entities and elements should be structured to assure accuracy, performance, understanding, operational, analytical, reporting, and data science efficiencies

Applied knowledge of cloud computing and RPA programming/machine learning

Applied knowledge of data modeling principles (e.g., dimensional modeling and star schemas)

Must be a self-directed professional with the ability to work effectively in a fast-paced demanding environment, handle multi-tasks, problem solve and effectively follow-through

Work collaboratively with internal partners/departments

Preferred

Hands-on experience with modern data lake/lakehouse patterns, S3-based pipelines, Glue, Step Functions, Athena, or event-driven orchestration

Real-world implementation experience to be able to articulate tradeoffs between architectural approaches like snapshotting vs diffing vs time-travel

High level of comfort and willingness to be working at the infrastructure or developer tooling level, writing Python-based ELT logic, using SDKs like Boto3 or CDK, or contributing to CI/CD automation and platform tooling

Able to contribute to foundational platform design or reusable framework development

Comfortable working in ambiguity-heavy environment that requires creating new patterns and processes

Ability to collaborate with other data engineers on SQL transformation logic and business rules in parallel with establishing core pipeline and platform

Someone who can help drive the modernization of our platform, through IaC, scalability, and NAWS automation

Demonstrated taking initiative on own (self-directed) in past roles (e.g., manually deploying CloudFormation templates, standing up some automation)