Senior Analytics Engineer

As a Senior Analytics Engineer, you will be part of the Data Science and Analytics team that supports enterprise data science efforts and exploring GenAI integration points. The goal of this team is to deliver end to end data science and artificial intelligence data products that drive business value. You will be working directly with data scientists, data analysts, project managers, and analytical engineer peers together as a unit to deliver to business partners. A successful candidate will have a robust background working with traditional database systems in SQL and has used python in a production fashion for ETL or data science model support. You will be required to dive in deep to complex business data domains and understand business relationships through the company’s legacy and cloud data environments to deliver a seamless data product to our data science teams, along with supporting a data science platform for ongoing business delivery. A successful candidate will be one who actively creates time to learn new things and enjoys being on the cutting edge of technology and does not shy away from experimentation.

Primary Duties & Responsibilities:

Lead day-to-day work to acquire, analyze, combine, synthesize, and structure data with clear definitions and sources for analytical consumption
Consult with data consumers to identify meaningful datasets for analytical consumption
Lead day-to-day development of data products using continuous deployment and integration practices
Apply engineering best practices in order to analyze, design, develop, deploy and support data analytics products
Participate in agile story authoring, sizing, and demo sessions for product features
Lead code reviews and provide feedback to the team
Mentor junior engineers

Qualifications:

Bachelor's Degree
4-5+ years of professional experience
4+ years of professional experience in data acquisition, cleansing, feature engineering, debugging and software documentation, using modern languages and practices
Experience using continuous integration and deployment concepts
At least 3+ year experience with specific technical requirements/platforms will vary (i.e. AWS data science and analytics cloud technologies such as S3, EMR, Spark, PySpark, RDS, JupyterLab, Sagemaker, etc., data warehouses suchas Redshift or Snowflake; and digital analytics tools such as Adobe Analytics and Heap)
Proficiency navigating various types of database models and DBMS’s to create data sets for analytics and model training and development
Experience developing solutions using agile methods
Experience with Testing or data accuracy/quality designs
Strong Python and cloud compute skills
Strong SQL skills and background in ETL
Deep understanding of distributed data systems and applications
Proficiency with data lake design and implementation considerations such as columnar storage formats (Parquet) and partitioning
Deep understanding of analytics, data science applications, and machine learning systems
Development of requirement driven data models such dimensional and relational
Deep understanding of the business domain and the meaning of the data in it to accurately identify key and associated data which can be extracted, transformed and loaded for effective consumption downstream
Develop the data flow so that data can be shared across the company with one source of truth being the goal
Understanding and awareness of regulations around the use of PII data
Ability to provide thought leadership throughout the local scrum team
Ability to explain technical solutions to technical teams and non-technical teams
Exceptional analytical, conceptual, and problem-solving skills
Excellent written and verbal communication skills
Preferred: Experience with Agile methodologies, experience in DevOps environment, Experience with user adhoc tools: power bi, business objects, Tableau; Consulting experience

Compensation Range: $100,030 to $185,770

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