Revel IT
Overview
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 productsspanning 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 Data Engineering & Pipeline Development 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. Engineering (Supporting Skills) 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. Expert-level proficiency (10/10)
in: 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.
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 productsspanning 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 Data Engineering & Pipeline Development 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. Engineering (Supporting Skills) 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. Expert-level proficiency (10/10)
in: 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.