SAP Data Engineer (Freelancer)

About the role

Freelancers drive the SAP extraction and loading work for enterprise data platform modernization on AWS. Production systems run, legacy systems retire, and measured outcomes emerge from clearly defined interfaces. You partner with a PySpark engineer while the agent platform handles repetitive discovery and validation. This role focuses on enabling data platform modernization by ensuring that data moves reliably from core SAP systems into the cloud data lake. You will be responsible for extracting data from SAP HANA and landing it in Amazon S3 using robust and incremental methods. The work requires a precise understanding of source systems and the semantics of the data being moved. You will collaborate closely with analytics and engineering teams to ensure that business users receive timely and accurate datasets. Your contributions will directly support the retirement of legacy systems and the adoption of modern cloud patterns.

Key facts

What you'll do

Extraction tooling selects the method per source system, and data lifts cleanly and incrementally from SAP HANA into S3.

This work ensures business users receive consistent, incremental datasets without repeated manual intervention.

You define the S3 contract in collaboration with agent-driven discovery and validation, while mapping semantics precisely for each source.

Requirements are translated into technical specifications that guide the extraction and loading process on AWS.

You work with a PySpark engineer to align transformation logic and cutover timing for data movement.

The agent platform manages repetitive discovery, validation, and test generation so you can focus on high-value decisions.

You maintain clear boundaries between extraction, transformation, and consumption layers to keep data flows stable.

You communicate interface changes and data contracts to stakeholders to prevent misalignment during modernization.

You ensure that data pipelines remain reliable as source systems evolve or are retired over time.

You contribute to documentation that supports long-term maintainability and knowledge transfer.

Requirements

The posting states a bachelor's degree requirement. A relevant degree is required, ensuring foundational preparation for the role.

Deep SAP knowledge enables extraction from SAP HANA and loading into BW / BW4HANA on AWS with correct semantics.

You understand data pipeline design for cloud platforms, specifically S3, AWS integration, and SAP connectivity patterns to avoid stalling modernization.

Strong attention to detail is necessary to ensure that data types, keys, and relationships are preserved during extraction.

You are comfortable working with structured data models and defining schemas that downstream teams can rely on.

Clear communication with customer teams supports alignment with a PySpark engineer on transformation workloads and cutover timing.

Reliability and ownership are essential, as you will be responsible for the correctness of data movement into the data warehouse.

You can interpret requirements and convert them into technical tasks that integrate with automated agent platforms.

Practical notes

Refer to the official apply page for application instructions and current expectations.

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

Data platform modernization for SAP on AWS depends on precise extraction, semantic mapping, and reliable loading into warehouse structures. Agent platforms can automate repetitive discovery, validation, and test generation, leaving engineers to choose tooling and own interface contracts. Successful cloud data pipelines rely on clear boundaries between extraction, transformation, and consumption layers to keep work flowing.

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.