MIA Data Engineer - Merchant Intelligence Team (Remote)

As a MIA Data Engineer at Constructor, your main responsibilities include enhancing the dashboard experience for merchandizers by building actionable analytics that drive e-commerce KPIs. You will also conduct data exploration to understand user behavior, implement end-to-end data pipelines for real-time analysis, and participate in product research and development. Your role will bridge the gap between technical data preparation and delivering insights for non-technical users.

To qualify for the MIA Data Engineer role at Constructor, candidates should be proficient in SQL and have experience in exploratory data analysis using Python, particularly with libraries like pandas and numpy. Familiarity with big data technologies such as Spark, Hive, and Databricks is also important. Strong communication skills are essential for translating complex data concepts into clear insights for business stakeholders.

In the role of MIA Data Engineer at Constructor, you will work with a variety of tools and technologies, including SQL, Python, and Pyspark for data analysis and manipulation. You’ll also be using Clickhouse for analytics and Cube.js for building dashboards. Familiarity with containerization tools like Docker and version control systems such as Git will further aid in streamlining your work.

Constructor offers a fully remote work environment, allowing you to choose where you live and work. We believe in the importance of work-life balance, which is why we provide unlimited vacation time and encourage everyone to take at least three weeks off per year. You'll be part of a supportive team that values learning and growth, with a training and development budget refreshed each year.

The MIA Data Engineer is vital to the Merchant Intelligence Team at Constructor as they work on enhancing the dashboard that merchandizers use to analyze shopping behavior and optimize their strategies. By building analytics that provide crucial insights and implementing effective data pipelines, you will help increase satisfaction and retention for merchants, ultimately aiming to meet and exceed their e-commerce goals.

When designing data pipelines for real-time analytics, I focus on understanding the business requirements first. This involves collaborating with stakeholders to define key metrics, data sources, and frequency of updates. I then choose appropriate technologies, ensuring that scalability and reliability are prioritized. Lastly, I test the pipeline thoroughly to identify bottlenecks and refine it before deployment.

My experience with SQL has primarily involved writing complex queries to extract and analyze data from various databases. To optimize queries, I focus on indexing frequently queried columns, rewriting queries for efficiency, and making sure to use best practices, such as avoiding SELECT * and limiting results only to what is necessary. Analyzing execution plans has also been a key part of ensuring optimal performance.

For exploratory data analysis, I employ various visualization techniques using libraries such as Matplotlib and Seaborn in Python. This allows me to identify trends, anomalies, and correlations in the data. I also utilize descriptive statistics to summarize the data and often incorporate techniques like clustering or dimensionality reduction to uncover deeper insights.

Handling data quality issues involves a proactive approach where I perform data validation checks at the point of data ingestion. If I encounter issues, I work on identifying the root cause, whether it's incorrect data entry or system errors. I then focus on cleaning the data, implementing automated checks, and ensuring that the data pipeline has fail-safes in place to mitigate similar issues in the future.

In a previous project, I developed a dashboard for non-technical users to track e-commerce metrics. Understanding that my audience was not data-savvy, I utilized simple visualizations and avoided technical jargon. I broke down the insights into actionable items, highlighting key metrics and trends that would directly impact their roles, making the information accessible and impactful.

I have hands-on experience with big data technologies like Apache Spark for handling large-scale data processing. I’ve used it for building pipelines that analyze vast amounts of data efficiently. In addition, I have worked with Hive for querying large datasets and Databricks for collaborative development of machine learning models.

Prioritizing tasks in multi-project environments involves assessing the business impact and urgency of each task. I utilize tools like Trello or Asana to track my tasks and set deadlines. Regular communication with stakeholders also helps in understanding which projects need more focus at any given time, ensuring I'm aligned with team goals.

Feedback is crucial in the development process. I actively seek it from peers and stakeholders throughout projects to ensure the work aligns with expectations and to identify potential areas for improvement. Constructive feedback helps me refine my analyses and develop better insights, increasing overall project success.

Staying updated in data analytics is essential for me, so I engage with communities through forums and social media, read industry blogs, and subscribe to newsletters focusing on data science. I also participate in webinars and online courses to continuously enhance my skills and understand emerging technologies and methodologies.

My motivation to work in the e-commerce data space stems from the dynamic nature of the industry and the opportunity to impact customer experiences positively. The ability to derive insights that can directly influence business decisions excites me, and I find it rewarding to see how data can drive innovation and improve a company's performance.