Data Analyst

Ames Construction•Burnsville, MN

1d•$80,000 - $105,000•Onsite

About The Position

Ames Construction is seeking an entry-level Data Analyst to join their team and support the modernization of the company’s data stack using Azure Databricks, GitHub, and Power BI. This role is designed for individuals early in their careers who are passionate about data engineering, analytics, AI, and cloud-first modernization. The successful candidate will gain practical experience across the entire data lifecycle, including ingestion, transformation, modeling, and reporting, contributing to the development of scalable and governed datasets and analytics outputs.

Requirements

Bachelor’s degree in data science, Statistics, Computer Science, Business, or equivalent work experience.
Internship, academic, or project-based experience in data engineering, analytics engineering, or a related field, including basic data modeling and relational database concepts.
Demonstrated ability to build or support data workflows end-to-end (ingestion through transformation to reporting) in a way that improves data reliability and usability.
Experience in building and supporting data visualizations, such as Power BI, Excel, or Databricks Dashboards.
Familiarity with Github-based workflows and CI/CD fundamentals (branching, pull requests, code reviews) and basic monitoring/data-quality practices.
Strong skills in coding languages such as SQL, Python, or Py-Spark for data querying and extraction, transformation, and loading (ETL/ELT) processes across Lakehouse and warehouse environments.
Introductory experience with statistical analysis tools (e.g., Python, R) and data processing frameworks as well as working with structured and unstructured data.
Understanding data quality assurance practices and data validation techniques.
Demonstrates strong problem-solving skills and a detail-oriented mindset when working with data and code.
Proactively identifies data issues and seeks guidance to resolve them.

Nice To Haves

Familiarity with end-to-end data platforms, such as Databricks, Azure, or Google Cloud, is a plus.
Knowledge of custom app building via Microsoft Power Apps and Databricks Apps.
Familiarity with using AI-assisted tools to improve productivity and code quality while following data security and governance standards.

Responsibilities

Building data pipelines and integrations across various data sources to a cloud platform (Lakehouse, Data Warehouse) to support data transformation and modeling using SQL, Python, and AI, as well as assisting with custom app support.
Implementing data validation routines and monitoring data integrity across systems.
Debugging and resolving issues related to data quality, pipeline reliability, and performance across the data stack.
Contributing to data governance efforts by tagging and classifying datasets, maintaining metadata, and supporting compliance with organizational standards using Databricks Unity Catalog.
Designing and publishing data models, schemas, and storage to simplify data access for business users.
Supporting the creation and maintenance of reports and dashboards using Power BI, other visualization tools, and AI, ensuring accuracy, user-friendliness, and alignment with stakeholder requirements.
Partnering with security and infrastructure teams to secure and request access from data to reporting via Azure Key Vault, Databricks, custom apps, and Power BI.
Engaging with business users across departments to understand data needs and provide initial support for data requests.
Documenting requirements, assisting in scoping tasks, and escalating complex requests to senior team members.
Maintaining clear and organized documentation of data sources, pipeline logic, and reporting processes.
Learning and applying best practices for data engineering, including modular coding, version control, and platform-specific standards.
Actively developing technical skills in Databricks, Py-Spark, SQL, and Python.
Understanding how data engineering contributes to broader organizational goals such as data democratization, AI readiness, and strategic decision-making.