Analytics Engineer, What is it and What Do They Do
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Analytics Engineer, What is it and What Do They Do
Big congratulations to our alumni, Yu-han Chen, for joining The Estée Lauder Companies Inc. Online as Senior Staff Analytics Engineer.
Quoted from Gitlab, "Analytics Engineers sit at the intersection of business teams, Data Analytics and Data Engineering and are responsible for bringing robust, efficient, and integrated data models and products to life. Analytics Engineers speak the language of business teams and technical teams, able to translate data insights and analysis needs into models powered by the Enterprise Data Platform. The successful Analytics Engineer is able to blend business acumen with technical expertise and transition between business strategy and data development.
Responsibilities
As a team member responsible for helping to bridge the gap between business and technology, the Analytics Engineer role requires equal amounts business acumen and technical acumen.
- Collaborate with team members to collect business requirements, define successful analytics outcomes, and design data models
- Build trust in all interactions and with Trusted Data Development
- Serve as the Directly Responsible Individual for major sections of the Enterprise Dimensional Model
- Design, develop, and extend dbt code to extend the Enterprise Dimensional Model
- Create and maintain architecture and systems documentation in the Data Team Handbook
- Maintain the Data Catalog, a scalable resource to support Self-Service and Single-source-of-truth analytics
- Document plans and results in either issue, MRs, the handbook, or READMEs following the GitLab tradition of handbook first!
- Implement the DataOps philosophy in everything you do
- Craft code that meets our internal standards for style, maintainability, and best practices (such as the SQL Style Guide) for a high-scale database environment. Maintain and advocate for these standards through code review.
- Approve data model changes as a Data Team Reviewer and code owner for specific database and data model schemas
- Provide data modeling expertise to all GitLab teams through code reviews, pairing, and training to help deliver optimal, DRY, and scalable database designs and queries in Snowflake and in Periscope.
Requirements
- Ability to use GitLab
- Ability to thrive in a fully remote organization
- Positive and solution-oriented mindset
- Comfort working in a highly agile, intensely iterative environment
- Self-motivated and self-managing, with task organizational skills
- Great communication: Regularly achieve consensus amongst technical and business teams
- Demonstrated capacity to clearly and concisely communicate complex business activities, technical requirements, and recommendations
- Demonstrated experience with one or more of the following business subject areas: marketing, finance, sales, product, customer success, customer support, engineering, or people
- 4+ years in the Data space as an analyst, engineer, or equivalent
- 4+ years experience designing, implementing, operating, and extending commercial Kimball enterprise dimensional models
- 4+ years working with a large-scale (1B+ Rows) Data Warehouse, preferably in a cloud environment
- 2+ years experience building reports and dashboards in a data visualization tool
- 1+ years creating project plans to identify tasks, milestones, and deliverables
Levels
Analytics Engineer (Intermediate)
The Analytics Engineer reports to the Manager, Data.
Analytics Engineer Job Grade
The Analytics Engineer is a grade 6.
Analytics Engineer Responsibilities
- The Analytics Engineer Responsibilites are listed above.
Analytics Engineer Requirements
- The Analytics Engineer Requirements are listed above.
Senior Analytics Engineer
The Senior Analytics Engineer reports to the Manager, Data.
Senior Analytics Engineer Job Grade
The Senior Analytics Engineer is a grade 7.
Senior Analytics Engineer Responsibilities
Responsibilites for the Senior Analytics Engineer extend the Analytics Engineer (Intermediate) job. In addition:
- Own one or more stakeholder relationship in Go To Market, Research & Development, or General & Administrative business functions
- Serve as Data Model subject matter expert and data model spokesperson, demonstrated by the ability to address questions quickly and accurately
- Advocate for the Data Quality Program and Trusted Data to help ensure all data is profiled, reviewed, and accurate to support critical decisions
- Guide Work Breakdown Sessions
- Organize and Plan quarter-long development initiatives per the Data Team Planning Drumbeat
Senior Analytics Engineer Requirements
Requirements for the Senior Analytics Engineer extend the Analytics Engineer (Intermediate) job. In addition:
- 6+ years in the Data space as an analyst, engineer, scientist, or equivalent
- 2+ years managing the same data model system over time, evolving the model to meet new business requirements
- Demonstrated experience leading 4 or more analytics projects from beginning to operationalization
- Demonstrated experience desinging and socializing Entity Relationship Diagrams and reference SQL scripts to scale data acumen and adoption
- Experience working with multiple commercial data warehouses, ETL tools, and data visualization tools
- Extenstive experience in 2 or more major data subject areas (marketing, sales, finance, product, people, etc.)
Staff Analytics Engineer
The Staff Analytics Engineer reports to the Manager, Data.
Staff Analytics Engineer Job Grade
The Staff Analytics Engineer is a grade 8.
Market Justification: From a survey data perspective 98 companies have this role with an average of 3 employee incumbents in all industries. In tech there are 33 companies reporting an average of 2 employee incumbents. The business justification for Analytics Engineer Staff and Principal job grades is to retain and develop deep technical talent by establishing Individual Contributor focused career paths for our team members who do not want to move into Data People Management.
Despite residing in the Finance Division, all Data job families are deeply technical in nature and require knowledge of databases, SQL, and modeling. Education in a technical field, typically Computer Science, Mathematics, Management Information Systems, or Data Analytics is typical for individuals in Data careers.
At GitLab, the Analytics Engineer role is critical to support the growing Data Program because it helps glue together the business-facing Data Analyst roles with the technology-focused Data Engineering roles by creating data solutions for both roles. The Analytics Engineer is a specialized in dbt, which GitLab has chosen as the standard for developing Trusted Data Models.
Staff Analytics Engineer Responsibilities
Responsibilites for the Staff Analytics Engineer extend the Senior Analytics Engineer job. In addition:
- Help promote data innovation across GitLab with a willingness to experiment and to confront hard and complex problems
- Identify and resolve impediments to efficiency and enable the entire Data Program to iterate faster
- Review and improve the data system as a whole, inclusive of data model designs, process flows, and end use cases
- Research new data engineering and analytics methodologies with minimal guidance and support from other team members
- Regularly participate in the Data Community/Industry outside of GitLab through writing, speaking, and/or networking
- Organize and Plan multi-quarter initiatives and develop the Enterprise Model Roadmap
- Help create a sense of psychological safety in the department
Staff Analytics Engineer Requirements
Requirements for the Staff Analytics Engineer extend the Senior Analytics Engineer job. In addition:
- Demonstrated experience leading 2 or more multi-department analytics projects from inception to operationalization
- Demonstrated proficiency with data system design, including databases, schema, marts, aggregates, and views
- Experience introducing a new tool or technique to a multi-person team, leading to measurable productivity improvement
- Experience with data access and security techniques, both inside and outside of a data warehouse
- Experience creating data pipelines in support of near real-time event stream processing
- Presented multi-quarter development roadmaps to non-technical audiences
Staff Analytics Engineer Specializations
Specializations within the Staff Analytics Engineer extend the Senior Analytics Engineer job:
- Staff Analytics Engineer, Data Architect:
- Sets data architecture principles, standards and guidelines
- Continuously reviews current data modelling principles and initiate any improvements to enable the implementation of the intended architecture
- Creates an inventory of the data and tools needed to implement a scalable data architecture.
- Staff Analytics Engineer, Technical Lead:
- Sets the technical direction for data and cross-functional projects
- Coordinates the technical effort during design and development and resolves technical disagreements
- Manages the technical quality of team deliverables
Principal Analytics Engineer
The Principal Analytics Engineer reports to the Manager, Data or Director, Data & Analytics.
Principal Analytics Engineer Job Grade
The Principal Analytics Engineer is a grade 9.
Market Justification: While there is limited supporting survey data for a grade 9, the same market justification for a Staff Analytics Engineer holds true for a Principal Analytics Engineer. In addition, Analytics Engineering is a relatively new job family in the Data space and is not as mature as the well-established Data Analysis, Data Engineering, and Data Scientist job families. Despite this, the Analytics Engineering job family is growing quickly and there are reasonable analysis to support the addition of new job grades:
- From a survey data perspective 11 companies have this role with an average of 1 employee incumbent in all industries.
- a LinkedIn search on 2021-08-16 identified 3 Principal Analytics Engineers within the tech sector.
- Companies which support the Analytics Engineer job family include: Netifly, Miro, Spotify, Netflix, Frame.io, Slalom, Pluralsight, and dbt.
Principal Analytics Engineer Responsibilities
Responsibilites for the Principal Analytics Engineer extend the Staff Analytics Engineer job. In addition:
- Lead major strategic data projects and initiatives, spanning 6 months or more
- Interface with Senior leadership to design, plan, and implement strategic data projects
- Willingness to experiment and to confront the hardest or most complex problems
- Attain a measurable positive impact on the performance of multiple team members and teams
- Regularly participates in the Data Community/Industry outside of GitLab through writing, speaking, and networking
- Provide mentorship to help team members grow their technical and business capabilities
Principal Analytics Engineer Requirements
Requirements for the Principal Analytics Engineer extend the Staff Analytics Engineer job. In addition:
- Demonstrated experience leading an analytics initiative that significantly improved business performance, acknowledeged by executive staff
- Ability to work productively as a Contributor in any Data Job, including Data Analysis, Data Engineering, and Data Science
- Experience with data access and security techniques, both inside and outside of a data warehouse
- Experience creating data pipelines in support of near real-time event stream processing
- Recognized in the industry as a result of publications, seminars, presentations, or equivalent
Performance Indicators
- Dimensional Model MRs Per Milestone
- Handbook Update Frequency
- % of Data Warehouse queries supported by Enterprise Dimensional Model >= 75%
Career Ladder
We are evaluating the addition of levels beyond the Senior level. Currently, beyond the Senior Analytics Engineer level the next step is to move to the Manager, Data job family. "
Based on Google search result, "Senior Analytics Engineer
The Estée Lauder Companies Inc.
Full-time
Job highlights
Identified by Google from the original job post
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Qualifications
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You are an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences
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1+ years of experience (may be substituted with a graduate degree) in Analytics, Data Science, or Software Engineering, including expertise with descriptive statistical analysis and data transformation
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Experience with analytics software such as Looker, PowerBI, Tableau, or Apache Superset
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Track record of delivering data-driven products and insights
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Experience working with large-scale cloud database systems (e.g., BigQuery)
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Experience in analyzing, validating, and transforming large datasets
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Proficiency in deploying data-intensive solutions
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Familiarity with the software development lifecycle
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Ability to effectively and concisely communicate technical concepts
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Hands-on experience in crafting compelling data visualizations to inform business decisions
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Comfortable working with ambiguous and dynamic business requirements
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Demonstrated ability to implement analytical and/or algorithmic solutions tailored to particular business needs and tested on large data sets
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Responsibilities
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As an Analytics Engineer at ELC Online, you will be responsible for transforming complex datasets into actionable insights
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Your primary role will be to develop, support, and continually improve reporting and analytics assets in platforms such as Looker, Data Studio and Power BI
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You will write efficient, clear code as well as accurate, rigorous technical documents to ensure deliverables meet customer needs and team goals
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You will collaborate with an interdisciplinary team of engineers, scientists, and product managers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of scalable solutions
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You will bring engineering maturity and structure to ambiguous business problems and address the challenges of product creation, development, and improvement with an appreciation for the behaviors and needs of our consumers
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You will partner with analysts to create compelling data visualizations and dashboards
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You will acquire data by building the necessary queries and pipelines
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You will automate data transformation, validation, monitoring, and alerting, with emphasis on maintainability, robustness, and scalability
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You will develop ETL/ELT processes that comply with the computational demands, accuracy, and reliability of the relevant processes at various stages of production
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Develop scalable systems to expand reporting capabilities, facilitate ad hoc analysis, and improve data-driven decision-making at all levels of the business
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Utilize code (SQL, python, etc.) and apply engineering, reporting, and visualization expertise to solve business reporting problems
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Develop scalable tools to drive automation and optimize business operations
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Work with large, complex data sets
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Solve difficult data transformation problems with efficiency, applying advanced analytical methods as needed
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Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations
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Build analysis and reporting pipelines iteratively to provide insights at scale
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Develop comprehensive knowledge of ELC data structures and metrics, advocating for changes where needed for product development
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Maintain standards for writing clean, organized code and documentation to streamline data and analytics workflow
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Provide subject matter expertise and mentorship for junior analytics professionals to deliver data-driven solutions in support of established roadmaps
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Drive and promote a culture of testing, observability, and scalability with a data-driven mindset and measurable approach
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Manage and continuously improve business user’s experience with data and reporting, including KPI development, data visualization, and communication of applicable insights to audiences at varying levels of technical sophistication
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Develop partnerships with engineering, data science, and product teams to deliver on cross-functional reporting, measurement, and testing efforts
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Solve analytical problems, and effectively communicate methodologies and results both verbally and in writing
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Actively participate in ELC’s diversity and inclusion agenda
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Act as a champion for inclusivity and the identification of bias in everything we do
Benefits
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Shift 1st (Day) Shift
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Full description
ETL/ELT
Responsibilities
Requirements
Qualifications
Job Online / E-Commerce
We also cross-referenced Udemy's opening of Senior Staff Analytics Engineer ( Analytics Engineering) .
About the Role
We are looking for an experienced analytics engineer to join our data team and push us towards a world with cleaner, more efficient data. You will be partnering with our data scientists and business stakeholders to understand our business and ensure that the data is ready for analysis and decision making.
Responsibilities
- Contribute high quality, documented, tested, and cleanly modeled data to our data warehouse.
- Help define and improve upon our analytics engineering standards through internal documentation and helpful peer reviews.
- Collaborate with data scientists, product managers, marketers and other data-driven internal stakeholders to identify opportunities for better data access and usage
- Develop and maintain data definitions for internally available data sources
Qualifications
- 6-8 years of experience working in the analytics field as a full-stack data analyst
- Excellent SQL skills with experience building tables in a production environment
- Knowledge in ETL performance tuning / query optimization
- Experience using git and command line within your data development
- Experience with data processing frameworks and tools such as Redshift, Hadoop, Hive, Spark, and Kafka
- Self-driven, highly motivated and able to learn quickly
- Strong communication skills to partner with and understand the needs of every data-consuming team in the company
In the first 30 days, you will...
- Partner with a teammate on their triage rotations
- Contribute to the code base by making updates to existing SQL pipelines
- Start working with all parts of our BI stack
In the first 60 days, you will...
- Handle your triage rotation without support from teammate
- Start to influence how the rest of the Business Intelligence team works
- Lead Decision Science office hours session
- Provide feedback on the team's onboarding processes based on your experience
In the first 90 days, you will...
- Work directly with a business stakeholder/data scientist to improve their ability to make decisions using data
- Build out a new data model from raw data ingestion through self-serve Looker model
- Be able to onboard new team members to the Business Intelligence team"
About Author
thomas.cheung@nycdatascience.com
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