Analytics Engineer

SRS Distribution Inc.•Lincoln, NE

About The Position

The Analytics Engineer is a critical role intended for an innovative, meticulous, and results-oriented professional with a proven background in data analytics and engineering. This position is primarily responsible for optimizing and expanding our data pipeline, developing robust data models to facilitate business interpretation, and driving data-focused decision-making across the organization. Our ideal candidate will seamlessly bridge technical and non-technical stakeholders, implement scalable solutions for large datasets, and excel in supporting the data science team while thriving in our fast-paced corporate setting.

Requirements

A Bachelor’s degree in Computer Science, Information Systems, or other related field. Advanced degrees or certifications are highly favored.
Minimum of 5 years of experience in Analytics engineering roles, ideally in the corporate sector.
Dynamic understanding of data analytics and knowledge of various SQL, Python, R, and other programming languages.
Comprehensive proficiency in using ETL tools for data processing.
Experience working with big data platforms such as Hadoop or Spark.
Demonstrable problem-solving capability and a strong analytical thinking capacity.
Excellent communication skills, with the ability to clearly articulate complex data understandings to both technical and non-technical personnel.
Proficient in functioning in high-pressure, strict deadline environments.
Experience in supporting data science teams with tasks such as data cleaning, data mining, and data visualization.
Proven track record in working with cross-functional teams and translating business requirements into data engineering initiatives.
Demonstrated ability to optimize data extraction, transformation, and loading processes strategically.

Nice To Haves

An advanced degree, such as a Master’s or PhD, in a field related to data sciences, computer engineering, or business intelligence.
Over seven years of experience in building and maintaining data pipelines, manipulating data sets, and managing large-scale data infrastructure in a corporate setting.
Advanced skills in SQL, Python, R, Java, and other programming languages relevant to data engineering.
Extensive experience with big data processing tools like Hadoop, Hive, and Spark, as well as data warehousing solutions like Redshift, Snowflake, or BigQuery.
Proficiency with data visualization tools such as Tableau, Power BI, D3.js, or QlikView, as well as BI tools such as Looker or SAS.
Knowledge of machine learning algorithms, predictive modeling, and statistical analysis techniques would be a definitive advantage.
Capacity to effectively pitch and present data-driven insights to senior stakeholders and drive the adoption of a data-first mindset within the organization.
Demonstrated ability to manage projects in a fast-paced environment, meet tight deadlines, and consistently deliver high-quality work.
Experience working with cross-functional teams, including Business Analytics, IT, and Finance, demonstrating a strong understanding of business strategy and operations.
Relevant professional certifications, such as Certified Analytics Professional (CAP) or Certified Data Management Professional (CDMP), would be highly desirable.
Track record of driving continuous improvement and innovation in a data management role.

Responsibilities

Design, build, and manage the Company's data pipeline using appropriate ETL tools to integrate a diverse range of data sources, facilitating data-driven insights for business improvement.
Collaborate effectively with both technical and non-technical stakeholders to accurately translate business needs into actionable data engineering tasks, ensuring solutions align with company objectives.
Create and refine data models that effectively simplify and elucidate complex business data, empowering colleagues and teams with clear, meaningful metrics.
Continually work to enhance data ETL processes for heightened efficiency and quality, ensuring swift, accurate data provision to all relevant departments.
Develop and maintain scalable data solutions capable of managing large data sets, fortifying the organization's capacity for comprehensive data analysis.
Provide essential support to the data science department, undertaking data cleaning, data mining, and data visualization tasks, and ensuring a consistent supply of reliable, high-quality data.
Play an active role in the design and enhancement of the company’s analytics infrastructure, promoting data-driven decision-making across the entire organization, and contributing to a culture of innovation and continuous improvement.
Stay abreast of emerging trends and developments in data engineering, applying this knowledge to the continuous improvement of the Company's data infrastructure.