Analytics Engineer II

generis tek•Philadelphia, PA

24d•Onsite

About The Position

The analytics engineer acts as a bridge between a data engineer and a data analyst. This position is primarily responsible for modeling raw data sets into curated, reusable, trusted data sets which power analytics across the enterprise. These data sets will serve as the single source of truth for data and enable self-service analytics. In addition to the development of data models, this role is responsible for maintaining data quality within these data sets via the use of monitoring, testing, and automation. An additional component of the role is to improve the effectiveness of data analysts and data scientists. This may be via providing technical expertise in query development, extending data models via the addition of new metrics, and/or consulting on software development practices. The Analytics Engineer owns the entire workflow of data associated with their domain; data pipeline development, ELT performance, timely loading of data sets, and maintenance. This role will work within various business units and partner with data analysts and data scientists to obtain a deep understanding of operational data and develop scalable data products which empower data-driven decision making across the enterprise.

Requirements

Minimum of six (6) years of experience working in data and analytics landscape.
Strong SQL, Data Modeling and Data Warehousing fundamentals.
Experience with software development practices; version control, code review, CI/CD.
Experience with data integration tools: DBT, Informatica, MS Integration Services etc.
Experience with big data toolset: Hadoop, Spark, Kafka, Hive, sqoop etc.
Experience working with Business Intelligence Tools (Business Objects) or Visualization tools such as Qlik, Tableau, Power BI etc.
Experience with stream-processing systems: IBM Streams, Flume, Storm, Spark-Streaming, etc.
Good hands-on experience with Linux (RHEL/Debian) operating system.
Ability to code with other scripting languages such as Python, Bash, groovy etc.
Experience consuming and building APIs.
Experience utilizing Agile methodology for development.
Bachelor’s degree in Computer Science, Computer/Software Engineering, Information Technology or related fields.

Nice To Haves

Experience with workflow orchestration tools such as Apache Airflow for scheduling and managing analytics and data transformation pipelines.
Experience using Python for data transformation, validation, or analytics workflows (nice to have).
Minimum of eight (8) years of experience working in data and analytics landscape.
One (1) year of experience working with at least one of the public cloud platforms such AWS/Azure/GCP.
Advanced SQL for analytics engineering, including complex transformations, aggregations, and performance tuning.
Strong dimensional data modeling skills, including design and implementation of fact and dimension tables.
Hands-on experience with Snowflake as a cloud data warehouse for analytics workloads.
Experience developing and maintaining analytics models using dbt, including testing and documentation.
Proven ability to refactor existing analytical data models to support reporting and dashboard migrations.
Experience supporting enterprise BI platforms, preferably Power BI, including semantic model alignment.
Strong analytical and problem-solving skills, with the ability to evaluate tradeoffs and recommend optimal modeling approaches.
Experience with Apache Airflow or similar workflow orchestration tools for scheduling and managing analytics pipelines.
Experience using Python for data transformation, validation, automation, or analytics workflows.
Familiarity with Agile or iterative delivery practices.
Experience with version control and modern analytics development practices (e.g., Git, pull requests, code reviews).
Experience supporting BI platform migrations (e.g., Qlik, Business Objects, or similar to Power BI).
Advanced degree in Computer Science, Informatics, Information Systems or another quantitative field.

Responsibilities

Collaborate with business subject matter experts, data analysts, and data scientists to understand/identify the opportunities to develop well-defined, integrated, re-usable data sets which power analytics.
Codify reusable data access patterns to speed up time to insights.
Perform Logical and Physical data modeling with an agile mindset.
Build automated, scalable, test-driven ELT pipelines.
Utilize software development practices such as version control via Git, CI/CD, and release Management.
Build data products using various visualization, BI tools and data science tools.
Collaborate with Data Engineers, DevOps engineers and architects on improvement opportunities for Data Ops tools and frameworks.
Implement data quality frameworks and data quality checks.
Help define analytical product roadmap to drive the business goals and superior quality outcomes.
Work with Data Scientists, Statisticians and Machine learning engineers to implement/scale advanced algorithms to solve health care, operational and quality challenges.
Work independently and effectively manage ones time across multiple priorities and projects.
Make recommendations about platform adoption, including technology integrations, application servers, libraries, and frameworks.
Participate in a shared production on-call support model.
Be a critical part of a scrum team in an agile environment, ensuring the team successfully meets its deliverables each sprint.