Senior Data Engineer

Jobtailor

Chicago (IL)

On-site

USD 130,000 - 180,000

Full time

14 days+

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Job summary

Jobtailor in Chicago, IL is seeking an experienced Data Engineer to build scalable data pipelines and data warehouses aligned with AWS cloud and BI tools. You will collaborate with product managers, data scientists, and operations to design data products and support analytics with robust ETL/ELT processes.

The role emphasizes Generative AI integration, high-volume data workflows, and agile practices. Strong SQL, Python, and cloud-native skills are essential for delivering reliable data solutions.

Qualifications

8+ years of data engineering or related analytics experience.
Design and build scalable data pipelines for data-driven decisions.
Strong understanding of SDLC and data warehousing concepts.
Experience in cloud data pipelines and high-volume workflows.
Hands-on AWS native tech: Glue, Lambda, Kinesis, S3, DynamoDB, Step Functions, Athena.
SQL, PL/SQL, NoSQL, and dimensional modeling expertise.
Python scripting for scalable data pipelines, plus CI/CD tooling.

Responsibilities

Define features and strategies for data products with cross-functional teams.
Develop and maintain production data pipelines and BI reporting.
Prototype and adopt emerging tools and Generative AI for data tasks.
Interface with stakeholders to gather requirements and deliver on time.
Work in an agile/Scrum environment and manage multiple projects.
Ensure data quality through cleansing, validation, and testing.

Skills

Data Engineering

ETL Development

AWS

SQL Proficiency

Python

Snowflake

Tableau

Big Data

Generative AI

API Development

CI/CD

Tools

AWS Glue

AWS Lambda

Snowflake

Tableau

Informatica IICS

GitHub Actions

Terraform

Kafka

AWS API Gateway

CloudFormation

Job description

Collaborate with product managers, data scientists, engineering, and program management teams to define product features, business deliverables, and strategies for data products
Collaborate with business partners, operations, senior management, etc on day-to-day operational support
Support operational reporting, self-service data engineering efforts, production data pipelines, and business intelligence suite
Interface with multiple diverse stakeholders and gather/understand business requirements, assess feasibility and impact, and deliver on time with high quality
Design appropriate solutions and recommend alternative approaches when necessary
Research, prototype, and recommend emerging tools and technologies across cloud data engineering and Generative AI
Enable current Generative AI initiatives through the development of pipelines that process unstructured data, transform text, integrate models, and validate generated outputs
Work with high volumes of data, fine-tuning database queries and able to solve complex technical problems
Contribute to multiple projects/demands simultaneously
Work in a fast-paced, collaborative, and iterative environment
Exercise independent judgment in methods and techniques for obtaining results
Work in an agile/scrum environment
Use state-of-the-art technologies to acquire, ingest and transform big datasets

Requirements

8+ years of experience within the field of data engineering or related technical work including business intelligence, analytics
Experience and comfortable solving problems in an ambiguous environment where there is constant change. Have the tenacity to thrive in a dynamic and fast-paced environment, inspire change, and collaborate with a variety of individuals and organizational partners
Experience designing and building scalable and robust data pipelines to enable data-driven decisions for the business
Very good understanding of the full software development life cycle
Very good understanding of Data warehousing concepts and approaches
Experience in building Data pipelines and ETL approaches
Experience in building high-volume data workflows in a cloud environment
Experience in building Data warehouses and Business intelligence projects
Experience in data cleansing, data validation, and data wrangling
Hands-on experience in AWS cloud and AWS native technologies such as Glue, Lambda, Kinesis , Lake Formation, S3, DynamoDB, Step Functions, Athena
Hands-on API development experience using AWS API Gateway, CloudWatch, Cognito, ECS, EKS, Fargate.
Experience with Business Intelligence tools like Tableau, Cognos, ThoughtSpot
Experience with relational databases like Oracle, Snowflake, including advanced SQL, Tasks/Streams, stored procedures, and performance tuning.
Hands-on experience with Snowflake Cortex AI, AI SQL functions or an equivalent production LLM platform.
Proficient in SQL, PL/SQL, NoSQL, relational databases (RDBMS), database concepts, and dimensional modeling
Hands-on experience building complex business logic and data ingestion and transformation (ETL/ELT) workflows using Informatica IICS or similar tools, AWS pipelines, Snowpipe, Streaming solutions, Kafka and other industry standard tools and processes is preferred.
Hands-on experience with Python/shell scripting and SQL for building scalable data pipelines.
Experience with CI/CD tools such as GitHub Actions and/or Jenkins, and infrastructure as code with Terraform and/or CloudFormation
Experience implementing LLM observability with Arize or a comparable platform is a plus.
Strong verbal and written communication skills
Demonstrate integrity and maturity, and a constructive approach to challenges
Demonstrate analytical and problem-solving skills, particularly those that apply to Data Warehouse and Big Data environments
Open-minded, solution-oriented and a very good team player
Passionate about programming and learning new technologies; focused on helping yourself and the team to improve skills
Effective problem-solving and analytical skills.
Ability to manage multiple projects and report simultaneously across different stakeholders
Rigorous attention to detail and accuracy

Core Competencies

Demonstrates expertise in building scalable data pipelines and data warehousing solutions, utilizing AWS cloud technologies and business intelligence tools. Proficient in data engineering practices, including data cleansing, validation, and transformation, while effectively collaborating with diverse stakeholders in a fast-paced environment.

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