Be ready to go over:

• Schema Design – Creating entity-relationship diagrams (ERDs) that accurately represent real-world business entities.
• SQL Optimization – Writing efficient queries, understanding indexes, and optimizing joins on large tables.
• Data Modeling – Choosing between relational (PostgreSQL) and non-relational storage depending on the use case.
• Advanced concepts (less common) – Graph databases for entity resolution and distributed database architectures.

Example scenarios:

• Designing a schema to track company hierarchies, subsidiaries, and historical address changes.
• Optimizing a query that joins a massive transaction table with a business profile table.

Communication & Problem Solving

Technical skills alone are not enough. Enigma looks for data scientists who can collaborate effectively and explain the business impact of their technical decisions.

Be ready to go over:

• Efficient Communication – Explaining your technical approach concisely during phone screens and technical interviews.
• Handling Feedback – How you respond to constructive criticism on your code or challenge submissions.
• Product Intuition – Aligning your data engineering choices with the ultimate needs of Enigma's clients.

Example scenarios:

• Explaining the trade-offs of your data challenge solution to an interviewer who challenges your parsing methodology.
• Walkthrough of a past project where you had to pivot your technical approach due to data limitations.

As a Data Scientist at Enigma, your daily work will be highly dynamic, bridging the gap between raw data engineering and analytical modeling. You will be responsible for:

• Ingesting and Processing Data – Building and maintaining scalable ETL pipelines to process gigabytes of public and proprietary business data daily.
• Entity Resolution – Developing and refining algorithms that match and merge duplicate records across disparate data sources to build a single source of truth.
• Database Architecture – Designing, implementing, and querying database schemas that support rapid data retrieval and complex analytics.
• Cross-Functional Collaboration – Partnering with software engineers, product managers, and data analysts to integrate your pipelines and models into Enigma's core API.
• Quality Assurance – Conducting rigorous dataset inspections to ensure the integrity, accuracy, and completeness of the data delivered to customers.

To be competitive for the Data Scientist position at Enigma, you must possess a strong foundation in both software engineering and data analysis.

Technical Skills

• Languages – Strong proficiency in Python and advanced SQL is mandatory.
• Data Engineering – Hands-on experience with web scraping (BeautifulSoup, Scrapy), data parsing, and building ETL pipelines.
• Databases – Deep understanding of relational databases (e.g., PostgreSQL) and schema design principles.
• Data Science Stack – Familiarity with Pandas, NumPy, and basic machine learning libraries (scikit-learn) is expected.

Experience & Soft Skills

• Experience – Typically 2+ years of professional experience in a data-centric role (Data Scientist, Data Engineer, or Quantitative Analyst).
• Communication – Ability to communicate complex technical concepts clearly and efficiently.
• Adaptability – Comfort working with highly ambiguous, unstructured, and messy datasets.

Must-Have vs. Nice-to-Have

• Must-Have – Strong Python scripting, proficient SQL querying, and practical experience with data scraping and parsing.
• Nice-to-Have – Experience with Spark or other distributed computing frameworks, knowledge of entity resolution algorithms, and familiarity with cloud infrastructure (AWS).

Q: What is the balance between machine learning and data engineering in this role? A: The Data Scientist role at Enigma leans heavily toward data engineering, ETL, and database design. While machine learning is used for tasks like entity resolution, your day-to-day focus will be on structuring, parsing, and linking large-scale datasets.

Q: Does Enigma provide feedback to candidates who do not pass the technical challenge? A: Yes. Many candidates have highlighted that Enigma provides exceptionally thoughtful and detailed feedback on technical challenge submissions, pointing out both strengths and areas for improvement. This reflects the company's professional and candidate-friendly culture.

Q: How difficult is the web-scraping and ETL task? A: Candidates generally describe the task as average in difficulty. It is highly practical, requiring you to scrape a dataset, parse it, and handle basic edge cases. Success depends on writing clean, runnable, and well-documented code rather than using overly complex algorithms.

Q: What is the work culture like for the data team? A: The culture is highly collaborative, technical, and mission-driven. Team members are passionate about data quality and solve complex identity-resolution problems together, valuing open communication and efficient execution.

• Prioritize Code Quality: When completing the take-home data challenge, write clean, modular Python code. Use meaningful variable names, include comments, and ensure your code is easy for the grading team to run and understand.
• Master Schema Design: Do not treat the database schema round as an afterthought. Practice designing relational schemas on a whiteboard, and be prepared to explain your foreign key relationships and indexing strategies.

• Show Your Data Intuition: During dataset inspection tasks, don't just write code. Explain what anomalies you are looking for (e.g., null values, formatting inconsistencies, duplicate records) and why they matter to the business.
• Be Ready for Behavioral Questions: The initial HR screen and subsequent rounds will test your communication efficiency. Be prepared to discuss your past projects using the STAR method (Situation, Task, Action, Result), focusing on your personal contributions.

The Data Scientist role at Enigma offers an exceptional opportunity to work on some of the most challenging data-linking and entity-resolution problems in the industry. By focusing your preparation on practical ETL pipelines, robust database schema design, and clear technical communication, you can position yourself as a top-tier candidate.

To maximize your chances of success, treat the take-home challenge as an opportunity to showcase your engineering discipline. Write clean code, document your assumptions, and be prepared to defend your technical decisions during subsequent interview rounds. You can find additional community insights, interview reviews, and preparation resources on Dataford to help you prepare.

The compensation data above reflects the competitive market rate for data professionals. At Enigma, your total compensation package will typically consist of a base salary, equity, and comprehensive benefits. Use this data to benchmark your expectations as you progress through the final stages of the hiring process.