Lead Data Engineer

Talentify; Madison (WI); Hybrid; USD 117,000 - 124,000; Full time

Job summary

Talentify in Madison, WI invites a lead data engineer to guide an engineering team through complex data pipelines and analytics initiatives. Hybrid work requires 2–3 days onsite to coordinate with product and business customers.

In this role you will drive data quality, security, and governance, while accelerating delivery of scalable ETL solutions in a modern data stack with GCP, Spark, and cloud-native tooling.

Qualifications

• Experience delivering customer-driven data engineering solutions.
• Proficient in GCP and data stores; SQL/NoSQL knowledge.
• Hands-on Python for ETL and data workflows.
• Knowledge of data modeling principles and star schemas.
• Experience with big data tools like Hadoop or Spark.
• Familiarity with open source ML toolkits (e.g. sklearn).
• Understand security, privacy, and data governance requirements.
• Experience with IaC tools and DevOps workflows.

Responsibilities

• Leads an engineering team to meet project deadlines and priorities.
• Supervises assigned data engineering team members and activities.
• Ensures the quality, completeness, security, privacy, and integrity of data throughout the data lifecycle.
• Documents critical workflows and operational support aspects of team's responsibilities.
• Develops deep understanding of data sources, granularity, availability, and limitations.
• Provides proactive technical oversight and advice to application architecture and development teams fostering re-use, design for scale, stability, and operational efficiency of data/analytical solutions.
• Creates maintainable, scalable code to load and manipulate data in the data warehouse.
• Facilitates communication upward and across project teams and business stakeholders.

Skills

GCP

SQL

Python

ETL

Data Warehousing

Data Modeling

Distributed Computing

CI/CD

Git

Tools

Docker

CloudFormation

Terraform

Hadoop

Spark

SparkML

H2O

Jenkins

Git

Job description

Pay rate range - $85/hr. to $90/hr. Hybrid - 2-3 Days in Office

Job Description

Primary Accountabilities

• Leads an engineering team to meet project deadlines and priorities.
• Supervises assigned data engineering team members & activities.
• Ensures the quality, completeness, security, privacy, and integrity of data throughout the data lifecycle.
• Documents critical workflows and operational support aspects of team's responsibilities
• Develops deep understanding of data sources, granularity, availability, and limitations.
• Provides proactive technical oversight and advice to application architecture and development teams fostering re-use, design for scale, stability, and operational efficiency of data/analytical solutions.
• Creates maintainable, scalable code to load and manipulate data in the data warehouse.
• Facilitates communication upward and across project teams and business stakeholders.

Specialized Knowledge & Skills Requirements

• Demonstrated experience providing customer-driven solutions, support or service.
• Must have GCP experience for this positon.
• In-depth knowledge of SQL or NoSQL and experience using a variety of data stores (e.g. RDBMS, analytic database, scalable document stores)
• Extensive hands‑on Python programming experience, with an emphasis towards building ETL workflows and data-driven solutions.
• Able to employ design patterns and generalize code to address common use cases.
• Capable of authoring robust, high quality, reusable code and contributing to the division's inventory of libraries.
• Expertise in big data batch computing tools (e.g. Hadoop or Spark), with demonstrated experience developing distributed data processing solutions.
• Knowledge of open source machine learning toolkits, such as sklearn, SparkML, or H2O.
• Solid data understanding and business acumen in the data rich industries like insurance or financial
• Applied knowledge of data modeling principles (e.g. dimensional modeling and star schemas).
• Strong understanding of database internals, such as indexes, binary logging, and transactions.
• Experience using tools for infrastructure-as-code (e.g. Docker, CloudFormation, Terraform, etc.)
• Experience with software engineering tools and workflows (i.e. Jenkins, CI/CD, git).
• Practical experience authoring and consuming web services.