Senior Data Analytics Engineer

Revel IT; Columbus (OH); On-site; USD 100,000 - 130,000; Full time

Job summary

A data solutions company is seeking an Analytics Data Engineer with deep expertise in building scalable data solutions on the AWS platform. Ideal candidates have at least 5 years of experience in data engineering and are 10/10 experts in Python and PySpark. Responsibilities include designing and developing data pipelines and collaborating closely with business stakeholders. The role requires strong skills in AWS services and a deep understanding of data warehousing and ETL processes. This position is suited for someone aiming to contribute significantly to enterprise decision-making.

Qualifications

• 5+ years of experience in data engineering, analytics engineering, or full-stack data development.
• Strong understanding of AWS services like Glue, EMR, and Redshift.
• Demonstrated experience with data warehousing, ETL/ELT, and data modeling.

Responsibilities

• Design and optimize scalable data ingestion pipelines using Python and AWS.
• Develop and maintain data transformation and curation processes.
• Collaborate with business users to translate analytical needs into technical solutions.

Skills

Python

PySpark

SQL

Data warehousing

ETL/ELT

Data modeling

Big data ingestion

CloudFormation

Tools

AWS Glue

AWS Lambda

AWS Redshift

AWS EMR

AWS Kinesis

Tableau

Job description

We are seeking a highly skilled Analytics Data Engineer with deep expertise in building scalable data solutions on the AWS platform. The ideal candidate is a 10/10 expert in Python and PySpark, with strong working knowledge of SQL. This engineer will play a critical role in translating business and end‑user needs into robust analytics products—spanning ingestion, transformation, curation, and enablement for downstream reporting and visualization.

You will work closely with both business stakeholders and IT teams to design, develop, and deploy advanced data pipelines and analytical capabilities that power enterprise decision‑making.

Key Responsibilities

• Design, develop, and optimize scalable data ingestion pipelines using Python, PySpark, and AWS native services.
• Build end‑to‑end solutions to move large‑scale big data from source systems into AWS environments (e.g., S3, Redshift, DynamoDB, RDS).
• Develop and maintain robust data transformation and curation processes to support analytics, dashboards, and business intelligence tools.
• Implement best practices for data quality, validation, auditing, and error‑handling within pipelines.

Analytics Solution Design

• Collaborate with business users to understand analytical needs and translate them into technical specifications, data models, and solution architectures.
• Build curated datasets optimized for reporting, visualization, machine learning, and self‑service analytics.
• Contribute to solution design for analytics products leveraging AWS services such as AWS Glue, Lambda, EMR, Athena, Step Functions, Redshift, Kinesis, Lake Formation, etc.

Cross‑Functional Collaboration

• Work with IT and business partners to define requirements, architecture, and KPIs for analytical solutions.
• Participate in Daily Scrum meetings, code reviews, and architecture discussions to ensure alignment with enterprise data strategy and coding standards.
• Provide mentorship and guidance to junior engineers and analysts as needed.
• Employ strong skills in Python, Pyspark and SQL to support data engineering tasks, broader system integration requirements, and application layer needs.
• Implement scripts, utilities, and micro‑services as needed to support analytics workloads.

Required Qualifications

• 5+ years of professional experience in data engineering, analytics engineering, or full‑stack data development roles.
• Python
• PySpark
• Strong working knowledge of:
• SQL and other programming languages
• Demonstrated experience designing and delivering big‑data ingestion and transformation solutions through AWS.
• Hands‑on experience with AWS services such as Glue, EMR, Lambda, Redshift, S3, Kinesis, CloudFormation, IAM, etc.
• Strong understanding of data warehousing, ETL/ELT, distributed computing, and data modeling.
• Ability to partner effectively with business stakeholders and translate requirements into technical solutions.
• Strong problem‑solving skills and the ability to work independently in a fast‑paced environment.

Preferred Qualifications

• Experience with BI/Visualization tools such as Tableau
• Experience building CI/CD pipelines for data products (e.g., Jenkins, GitHub Actions).
• Familiarity with machine learning workflows or MLOps frameworks.
• Knowledge of metadata management, data governance, and data lineage tools.

Seniority level: Mid‑Senior level

Employment type: Other

Job function: Information Technology

Industries: Electrical Equipment Manufacturing