Data Engineer

Description

We are looking for a Data Engineer to help strengthen financial decision-making by building reliable data and automation capabilities that support planning and analysis. This position works closely with finance leaders and business partners to turn strategic goals into scalable data solutions, improving visibility, speed, and confidence in reporting.

Responsibilities:

Design and maintain data pipelines that convert financial and operational information into trusted, analysis-ready datasets for planning and reporting.

Create scalable data models and structured finance data assets within the broader enterprise ecosystem to support consistent, governed analytics.

Develop automation solutions using tools such as Python, Excel, and workflow platforms to streamline recurring finance processes and increase team capacity.

Build near real-time data transformation processes that improve the timeliness and accuracy of performance visibility for stakeholders.

Apply AI-enabled techniques such as anomaly detection, forecasting support, and variance analysis to enhance financial insight generation.

Deliver self-service analytics tools and reporting frameworks that allow business users to explore key metrics and make informed decisions independently.

Establish documentation, data standards, and governance practices that improve maintainability, quality, and long-term adoption of solutions.

Monitor and support automated jobs and bot-driven processes, including troubleshooting, performance oversight, and control management.

Translate complex data findings into clear, executive-level outputs that help guide business strategy and financial priorities

Requirements

Bachelor's degree in Data Analytics, Computer Science, Finance, Accounting, Artificial Intelligence, Machine Learning, or a related discipline.
At least 3 years of experience in data engineering, analytics engineering, data analysis, or a technically focused finance environment.
Strong hands-on experience with Python and ETL development for scalable data and analytics applications.
Experience working with modern data platforms and data modeling concepts; exposure to Snowflake is preferred.
Knowledge of financial planning, reporting, or business performance concepts that support practical analytics solutions.
Background in building and maintaining data pipelines, workflow orchestration, and scheduled data processing jobs.
Familiarity with technologies such as Apache Spark, Hadoop, Kafka, and cloud or analytics tools used in enterprise data environments.
Relevant certifications in data, analytics, or cloud platforms such as Snowflake, Microsoft, or dbt are a plus.

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