Analytics Engineer, Sr

Senior Analytics Engineer at Realtor.com (View all jobs) 

Location: Austin, TX 

Who We Are

At http://Realtor.com , we have the most comprehensive and accurate coverage of real estate listings and the most engaged users across all the online real estate portals. Our mission is to make buying, selling, renting, and living in homes easier and more rewarding for everyone.

The Data Science and Analytics organization at http://Realtor.com sits at the heart of this mission. We process and analyze terabytes of data every day that enable decisions for millions of home buyers, sellers, renters, dreamers, and real estate professionals. Our goal is to use this data to make the home buying experience a breeze for our consumers. We empower them with the most up-to-date information on properties, help them find their dream homes in the least amount of time, and match them with the most suitable realtor to meet their unique, individual needs.

What you’ll do:

• Data modeling- model raw data into clean, tested, and reusable datasets. 
• Data transformation- removing inaccurate or corrupted data; aggregating data items into a summarized version; filtering information to get rid of irrelevant, duplicated, or overly sensitive data;joining two or more database tables by their matching attributes; and splitting a single column into multiple ones, to name a few.
• Data-associated documentation. Analytics engineers are often tasked with maintaining data documentation to ensure that everyone on the team uses the same definitions and language. This involves providing identifiable and understandable descriptions of data as well as exposing them in a way for all consumers to easily find answers to their queries. Experts document data at every stage, specifying the details of data features.
• Defining data quality rules, standards, and metrics. It is not rare for analytics engineers to take responsibility for data quality management.
• Setting software engineering best practices for analytics.
• Close collaboration with other team members. It is an important part of an analytics engineer’s job to work collaboratively with all stakeholders namely data engineers, business analysts, and data scientists to align business requirements with data assets.

How We Work:
We value creativity and innovation, supported by in-person collaboration. Our employees work in the office three or more days a week to foster a close-knit culture and drive collaboration.

What You’ll Bring:

• Education background- bachelor's in business analytics, data science, computer science, MIS, or similar technical degrees + 5 years of relevant work experience,  or master’s + 6 months of relevant work experience in corresponding domains, e.g., statistics, mathematics, computer science, software engineering, or IT.
• Strong SQL skills including query optimization, indexing, building tables in technologies such as Snowflake, Redshift, databricks, etc... Since the lion’s share of an analytics engineer’s duties will be creating logic for data transformations, writing lots of queries, and building data models, being an expert in SQL is a must.
• Strong data engineering skills including building pipelines, creating quality checks, scheduling jobs. Skills in tools such as dbt, airflow, etc…
• The dbt technology knowledge. As a rule, analytics engineers are expected to know how to work with dbt — a transformation command tool that allows implementing analytics code using SQL.
• Knowledge of software engineering best practices. Analytics engineers must be well aware of how to adopt software engineering best practices and apply them to analytics code.
• Git expertise. As the most commonly used version control system, Git should necessarily be within the tools a good analytics engineer is comfortable working with. It keeps track of any changes done to data and allows multiple users to make changes.
• Data engineering and BI tools knowledge. It is a big plus if your future analytics engineer has hands-on experience with tools for building data pipelines. The list may include data warehouses like Snowflake, Amazon Redshift, and Google BigQuery; ETL tools like AWS Glue, Talend, or others; Business Intelligence tools like Tableau, Looker, or equivalent.
• Interpersonal and communication skills. Being able to ask the right questions in an appropriate way is crucial to enable analytics engineers to excel in this career. They interact with different team members and other stakeholders on a regular basis, so employers should always check interpersonal skills.