AWS Data Engineer (Senior)
Job description
About the role
This role focuses on running production systems while legacy infrastructure is retired and outcomes are measured through contracted delivery. The hire will own the consolidation and migration of customer data infrastructure on AWS, completing these efforts in weeks using agent automation and senior judgment. Work engages live engagements where pipelines currently lack trust and warehouses remain unused, requiring steady hands to stabilize environments. The position centers on data platforms that transform raw information into decisions through carefully built pipelines. You will operate at the intersection of data engineering, analytics, and data science, collaborating closely with business teams. Nearly every modern company relies on data teams, from startups to banks, making clear communication and business impact essential. A strong portfolio of past analyses and delivery on contracted timelines matters more than degrees in many hiring decisions for this type of role.
Key facts
What you'll do
Production workloads move toward stable production on committed contract dates as pipeline builds target EMR and Glue, data models span Redshift, Snowflake, Athena, and Presto, and Airflow orchestration handles scheduling and monitoring. The platform verifies correctness through lineage and parallel-run validation so the team can cut over systems on committed contract dates without surprises. Troubleshooting of performance and reliability issues keeps migrated workloads running stably in production and enables retirement of legacy infrastructure that no longer serves the business. You will design and operate Airflow orchestration for reliable data workflows, ensuring fixed contract dates are met by owning critical architecture decisions and cutover execution where judgment is required. Customer data infrastructure on AWS is consolidated and migrated in weeks, with background in data modernization engagements or consulting required to navigate complex environments. The role involves absorbing routine discovery and validation tasks through agent-based automation, allowing the team to focus on higher-level design and business outcomes. You will partner with analysts, data scientists, and business stakeholders to ensure data platforms deliver timely, trustworthy insights that drive decisions.
Requirements
Hands-on experience covers five to seven years with data platforms and AWS data services, including EMR, Glue, Redshift, Snowflake, Athena, and Presto in production environments. Airflow orchestration must be designed and operated for reliable data workflows, with a track record of meeting fixed contract dates by owning architecture decisions and cutover execution where judgment is required. You must demonstrate background in data modernization engagements or consulting, with the ability to consolidate and migrate customer data infrastructure on AWS in weeks. Strong ownership of pipeline reliability, performance, and correctness is essential, supported by lineage and parallel-run validation strategies. The role requires comfort working directly with live engagements where pipelines lack trust and warehouses remain unused, turning these situations into stable, production-ready platforms. You should be able to interpret and translate business requirements into technical data flows that support contracted delivery timelines. Experience communicating uncertainty and business impact clearly to both technical and non-technical stakeholders is a hard requirement for success. A commitment to continuous learning is necessary given the fast-moving nature of cloud data platforms and agent-based automation.
Practical notes
This freelance role delivers real work on live engagements instead of staff augmentation into another team's backlog. Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Modern data platforms move large, complex datasets through distributed compute and storage layers. Agent-based automation absorbs routine discovery and validation tasks common in data platform work. Cloud data services on AWS and orchestration tools such as Airflow form the core technology environment for this type of role.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
About the company
Mactores is the agent-native AWS modernization firm. We ship AWS modernization to production in weeks, data platforms migrated, legacy applications and databases refactored, AI agents running against real data, for mid-market and lower-enterprise companies in financial services, healthcare and life sciences, internet and software, manufacturing, and TMEGS.