[All remote jobs](https://jobicy.com/jobs.md)Open role[![Mactores logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/cea77a13-221.jpeg)](https://jobicy.com/company/mactores.md)Remote opportunity at[Mactores](https://jobicy.com/company/mactores.md) # AWS Data Engineer (Senior) Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/mactores.md)Share19 Aug 2026Published28Listing views1Application actions18 Sep 2026Apply before Opportunity details ## About this role. AI SummaryMactores is seeking a senior AWS Data Engineer to modernize customer data platforms and deliver production-ready migrations under fixed delivery commitments. The role centers on designing data architectures, models, ETL pipelines, and safe cutover strategies using PySpark, SQL, EMR, Glue, Airflow, Redshift, Snowflake, Athena, and Presto. The engineer will work in a forward-deployed model with customer teams, balancing hands-on implementation, troubleshooting, and technical decision-making. Candidates need demonstrated experience in AWS data pipelines, data modeling, orchestration, and communicating complex technical work to both technical and non-technical stakeholders. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 4/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a technically demanding customer-facing modernization role requiring production AWS data engineering expertise across pipelines, warehousing, orchestration, and migration cutovers. The fixed-date delivery model and personal ownership of outcomes create a fast pace and require strong independent judgment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$157,500US market range$135k–$180k0$198k AI insightNo candidate compensation range is disclosed in the posting. These figures are estimated annual USD base-salary benchmarks for a senior AWS Data Engineer in the Seattle, US market, reflecting the role's customer-facing modernization scope and required AWS, PySpark, SQL, Airflow, and data-warehouse expertise. ## Core skills Skills and capabilities most closely associated with this opportunity. [AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[PySpark](https://jobicy.com/jobs?search_keywords=PySpark.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Amazon EMR](https://jobicy.com/jobs?search_keywords=Amazon%20EMR.md)[AWS Glue](https://jobicy.com/jobs?search_keywords=AWS%20Glue.md)[Apache Airflow](https://jobicy.com/jobs?search_keywords=Apache%20Airflow.md)[Data Modeling](https://jobicy.com/jobs?search_keywords=Data%20Modeling.md)[Amazon Redshift](https://jobicy.com/jobs?search_keywords=Amazon%20Redshift.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md) Cover letter sampleDear Hiring Team, I am excited to apply for the AWS Data Engineer (Senior) role at Mactores. My background in building production data pipelines with PySpark, SQL, AWS data services, and orchestration tools aligns well with your focus on delivering dependable modernization outcomes for customers. I would bring a practical, ownership-oriented approach to designing data models, optimizing pipelines, and managing safe migration cutovers while communicating clearly with technical and business stakeholders. I am particularly drawn to Mactores' forward-deployed model and commitment to shipping measurable production systems, and I would welcome the opportunity to contribute. Sincerely, Candidate Copy Sample interview questionsDescribe a production data pipeline you built using PySpark and Amazon EMR or AWS Glue.I would explain the source systems, transformation logic, storage and serving layers, scheduling approach, data-quality controls, monitoring, and measurable production outcomes. I would also describe how I addressed scale, schema evolution, retries, and operational ownership. How would you plan a migration from a legacy warehouse to Redshift or Snowflake while minimizing cutover risk? I would begin with source discovery, dependency mapping, target-model design, and a phased migration plan. I would use parallel runs and reconciliation checks to validate record counts, aggregates, data quality, and query behavior before a planned cutover, with rollback criteria documented in advance. What steps do you take to troubleshoot a slow or failing Spark pipeline? I first isolate whether the issue is caused by data skew, inefficient joins, partitioning, file sizes, resource configuration, external dependencies, or bad input data. I use Spark and platform metrics to validate the hypothesis, optimize the job through techniques such as repartitioning or broadcast joins where appropriate, and add observability to prevent recurrence. How do you design an Airflow workflow for reliable production operations? I create idempotent tasks with clear dependencies, retries, timeouts, alerting, and parameterized environments. I also include data-quality validation, sensible backfill behavior, lineage documentation, and failure handling that makes it clear whether operators can safely rerun a task or need to investigate upstream data. How do you communicate a technical architecture decision to a customer stakeholder who is not deeply technical? I frame the decision around the business outcome, options considered, trade-offs, cost or delivery impact, risks, and recommendation. I use concise diagrams and plain language, confirm assumptions and acceptance criteria, and document the decision so the customer can confidently sign off on the approach. Mactores is the agent-native AWS modernization firm. Most modernization work doesn’t ship, it stalls in pilots, slips a year, or lands at three times the budget. We exist to ship it: production systems running, legacy retired, outcomes measured. Our delivery is built on Aedeon, the agent platform built by Mactores’ founders’ sister company, which absorbs the repetitive 60–70% of engagement work, discovery, dependency mapping, validation, test generation, that traditional consulting bills human hours against. Forward-deployed engineers own the rest: architecture, judgment, and cutover, on dates we commit to in the contract. This is a senior role in our Data Platform Modernization pillar: consolidating and migrating customer data infrastructure on AWS in weeks, not quarters, at meaningfully lower engagement cost than traditional data consulting. Customers come to us after a data program has stalled pipelines nobody trusts, warehouses nobody runs new workloads on, a modernization that produced diagrams instead of production systems. Aedeon handles automated source discovery, schema mapping, lineage extraction, and parallel-run validation. You own what agents can’t: target architecture, data model decisions, pipeline design under real constraints, and the calls that make a cutover safe. You’ll build with PySpark and SQL on EMR and Glue, model for Redshift, Snowflake, Athena, and Presto, orchestrate with Airflow and your work will reach production, not a slide deck. ### What you will do? * Build and maintain data pipelines on Amazon EMR or Amazon Glue that run in production. * Design data models and end-user querying on Amazon Redshift or Snowflake, Amazon Athena, and Presto. * Build and maintain pipeline orchestration with Airflow. * Work with customer and internal teams to understand data needs and design the solutions that meet them. * Troubleshoot and optimize pipelines and data models until they hold up under real load. * Write and maintain PySpark and SQL scripts to extract, transform, and load data. * Document and communicate technical decisions to technical and non-technical audiences — customers sign off on what we ship. * Track new AWS data technologies and judge their impact on the systems we run. ### What are we looking for? * Bachelor’s degree in Computer Science, Engineering, or a related field. * 3+ years of experience working with PySpark and SQL. * 2+ years of experience building and maintaining data pipelines using Amazon EMR or Amazon Glue. * 2+ years of experience with data modeling and end-user querying using Amazon Redshift or Snowflake, Amazon Athena, and Presto. * 1+ years of experience building and maintaining pipeline orchestration using Airflow. * Strong problem-solving and troubleshooting skills. * Excellent communication and collaboration skills. * Ability to work independently and within a team environment. ### You are preferred if you have * AWS Data Analytics Specialty Certification * Experience with Agile development methodology ### How we work? We run a forward-deployed model. Senior engineers embed with the customer’s team, own outcomes from discovery through production, and carry the delivery commitment personally fixed dates, with Mactores absorbing overage cost for delays inside our control. Aedeon absorbs scale; you absorb judgment. That means less of your week goes to inventory spreadsheets and manual validation, and more goes to architecture, data modeling, and cutover strategy. The culture is casual and steers clear of rigid corporate habits. We measure ourselves by what ships. ### Compensation ### Additional Information Life at Mactores We care about creating a culture that makes a real difference in the lives of every Mactorian. Our 10 Core Leadership Principles that honor Decision-making, Leadership, Collaboration, and Curiosity drive how we work. 1. Be one step ahead 2. Deliver the best 3. Be bold 4. Pay attention to the detail 5. Enjoy the challenge 6. Be curious and take action 7. Take leadership 8. Own it 9. Deliver value 10. Be collaborative We would like you to read more details about the work culture on [https://mactores.com/careers](https://mactores.com/careers) The Path to Joining the Mactores Team At Mactores, our recruitment process is structured around three distinct stages: Pre-Employment Assessment: A series of evaluations of your technical proficiency and suitability for the role. Managerial Interview: The hiring manager engages with you in multiple discussions, 30 minutes to an hour each, covering technical skills, hands-on experience, leadership potential, and communication. HR Discussion: During this 30-minute session, you’ll have the opportunity to discuss the offer and next steps with a member of the HR team. Mactores provides equal opportunities in all employment practices. We don’t discriminate based on race, religion, gender, national origin, age, disability, marital status, military status, genetic information, or any other category protected by federal, state, and local laws. This applies to every part of the employment relationship, recruitment, compensation, promotions, transfers, disciplinary action, layoff, training, and social and recreational programs. Note: Please answer as many questions as possible with this application to accelerate the hiring process. Show more [Apply now >](https://jobicy.com/jobs/151146-aws-data-engineer-senior.md) > Annual salary information is not provided for this position. 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