Data & AI Engineer

MMD Services is an early-stage startup focused on transforming transaction-level business data for private equity and M&A. The Data & AI Engineer will build technology platforms and data infrastructure to enable actionable insights and drive strategic decision-making.

Responsibilities:

• Build Transaction-Level Data Platforms
• Design and develop scalable cloud-based data environments
• Build automated data pipelines from ERP, CRM, payroll, and financial systems
• Create reusable onboarding frameworks and connector architectures
• Transform fragmented business data into a trusted source of truth
• Support multi-client data environments across varying industries and technology stacks
• Build AI-Powered Data Infrastructure
• Develop AI-assisted workflows for data cleaning and normalization
• Create intelligent monitoring and anomaly detection systems
• Leverage LLMs and AI tools to automate repetitive engineering tasks
• Explore agentic workflows that improve data quality and operational efficiency
• Evaluate emerging AI technologies and incorporate them into the platform
• Partner with Clients
• Lead technical onboarding for new client engagements
• Extract and organize historical transaction data
• Work directly with finance, operations, and IT stakeholders
• Solve complex data challenges in real-world business environments

Requirements:

• 3–7 years of Data Engineering experience
• Familiar with invoice-level transaction datacubes for private equity owned companies
• Strong SQL and Python skills
• Experience building and maintaining production-grade data pipelines
• Experience with Snowflake, Databricks, or similar cloud data platforms
• Experience with ETL/ELT tools such as Fivetran, Airbyte, or custom integrations
• Familiarity with Airflow, Prefect, Dagster, or similar orchestration tools
• Experience leveraging AI tools within engineering workflows
• Ability to thrive in a fast-paced startup environment
• The strongest candidate is likely someone who has built and analyzed transaction-level data environments for Private Equity Firms
• PE Portfolio Companies
• Transaction Advisory Firms
• Financial Diligence Teams
• Manufacturing & Distribution Businesses
• Revenue Analytics & Business Intelligence Teams