Alignable, a remote-first, mission-driven Series C growth stage startup, is revolutionizing how small businesses connect and thrive. As North America's largest small business network with over 7 million businesses in 30,000+ communities, Alignable empowers business owners through a supportive and highly-engaged peer network that facilitates trusted relationships and quality referrals.

As a Senior Data Engineer at Alignable, you will leverage your expertise in data analysis and engineering to drive strategic business decisions. This pivotal role involves managing complex data sets, building scalable data pipelines, and developing data-driven models to enhance our platform. Collaborating closely with product management, design, marketing, and business partners, you will set and achieve strategic goals, impacting the entire company and its member base.

If you are passionate about supporting small businesses and using data to drive growth, this is the role for you. Let's get started with Interview Query to guide you through the interview process!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Alignable as a Data Engineer. Whether you were contacted by an Alignable recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, a recruiter from Alignable's Talent Acquisition Team will make contact to verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Alignable hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Alignable Data Engineer role is usually conducted virtually, including video conferences and screen sharing. Questions in this 1-hour long interview stage may revolve around Alignable’s data systems, ETL pipelines, and SQL queries.

For data engineering roles, take-home assignments regarding data infrastructure, analytics, and machine learning may be incorporated. Apart from these, your proficiency against data analysis, algorithm development, and system design may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at Alignable. Your technical prowess, including programming and data engineering capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Engineer role at Alignable.

Quick Tips For Alignable Data Engineer Interviews

• Understand Alignable’s Mission and Values: Alignable aims to empower small businesses through their networking platform. Be prepared to speak on how your skills and experience align with their mission.
• Brush Up on Data Engineering Tools: Be proficient in SQL, Pandas, Python, and familiar with tools such as Amplitude, Looker, and Redshift. These skills are crucial for the role.
• Be Ready for Problem-Solving Questions: Your ability to solve complex data problems and optimize data pipelines will be crucial. Practice as many data engineering scenarios and mock interviews as possible.

Typically, interviews at Alignable vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Develop a function precision_recall to calculate precision and recall metrics. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array and return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why might the number of job applicants be decreasing while job postings remain the same? You observe that the number of job postings per day has remained constant, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• Write a function to calculate sample variance from a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything suspicious about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• Write a function to return the median value of a list in O(1) time and space. Given a sorted list of integers where more than 50% of the list is the same repeating integer, write a function to return the median value in O(1) computational time and space. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks of the given student test score data layouts? You have student test score data in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in "messy" datasets.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain how a random forest generates its forest of decision trees. Additionally, discuss why you might choose random forest over other algorithms like logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? You are comparing two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of building such a model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

What is the average salary for a Data Engineer at Alignable?

Q: What is Alignable's mission?

Alignable is on a mission to empower small business owners by fostering connections and building a trusted online community. We believe that small businesses are stronger together and aim to support their growth and success through quality referrals and supportive peer networks.

Q: What will I be doing as a Senior Data Engineer at Alignable?

As a Senior Data Engineer, you will work with large and complex data sets to identify trends and insights that can drive business decisions. You'll develop and implement data-driven models and algorithms, design and maintain scalable data pipelines, and work closely with various stakeholders to achieve strategic goals.

Q: What technical skills and experience are required for this role?

You should have a minimum of 3 years' experience in both data analysis and engineering, with proficiency in SQL, Pandas, and other data tools. Familiarity with Amplitude, Looker, SEO tools, and large data cluster tools is a plus. You should also have experience in advanced data sleuthing, query optimization, and knowledge of data infrastructure pipelines.

Q: What makes Alignable a great place to work?

Alignable offers a dynamic, supportive culture with opportunities for career growth, autonomy, and flexibility. We celebrate diversity and are committed to an inclusive environment where creativity and innovation thrive. Benefits include competitive compensation, equity, health insurance, unlimited vacation, and a fully remote working environment.

Q: How can I prepare for an interview at Alignable?

To prepare for an interview, research Alignable's mission and role in empowering small businesses. Practice your technical skills and problem-solving abilities using Interview Query. Be ready to discuss your past experiences and how they align with the role you're applying for.

If you're passionate about empowering small businesses and leveraging the power of data to foster growth and innovation, Alignable offers an exciting and meaningful opportunity. As North America's largest small business network, our mission-driven approach and supportive, dynamic culture create the perfect environment for you to make a significant impact. Ready to bring your expertise in data engineering to the forefront of a thriving platform? Dive deeper with our Alignable Interview Guide. At Interview Query, we equip you with the tools, insights, and confidence to ace every aspect of your interview process. Discover all our company interview guides for robust preparation. If you have any questions, we’re here to help. Good luck with your interview journey!