Sr. Data Engineer

Total Wine & More•SSC - MD, MD

10d•$122,200 - $165,000•Onsite

About The Position

Total Wine & More is seeking a Senior Data Engineer to join our Technology team in our Bethesda, MD office. This role will be part of our enterprise data team and play a pivotal role in building the data infrastructure that powers analytics across the organization. In this role, you will design and operate the pipelines, platforms, and frameworks that deliver reliable, timely, and high-quality data to analytics engineers, analysts, and business users across merchandising, supply chain, marketing, and e-commerce. You will report to the Sr. Director, BI & Data Platform.

Requirements

Bachelor's Degree in Computer Science, Statistics, Engineering preferred or equivalent years of experience
3-6 years of experience in data engineering, building and operating production data pipelines at scale.
Expert-level SQL and strong programming skills in Python, with hands-on experience in Spark or similar distributed processing frameworks.
Deep experience with modern cloud data platforms (e.g., BigQuery, Microsoft Fabric, Snowflake, Databricks) and lakehouse architectures (e.g., Delta Lake, Parquet).
Experience with streaming and event-driven architectures (e.g., Kafka, Pub/Sub) and real-time or micro-batch ingestion patterns.
Strong command of version control (e.g., Git), CI/CD pipelines, and data testing/observability frameworks (e.g., Great Expectations, dbt tests).
Solid understanding of data warehousing, dimensional modeling, and ETL/ELT best practices.
Excellent communication skills and ability to work cross-functionally in a large, matrixed organization.

Nice To Haves

Familiarity with BI and analytics tooling (e.g., Power BI, dbt) and how downstream consumers use engineered data is a plus.

Responsibilities

Design, build, and maintain scalable, fault-tolerant batch and streaming data pipelines that ingest data from internal systems, third-party platforms, and APIs.
Develop and optimize ELT/ETL workflows using modern orchestration and processing frameworks (e.g., Spark/PySpark, Airflow, or notebook-based pipelines) across cloud data platforms.
Own the reliability, performance, and cost-efficiency of data infrastructure, including monitoring, alerting, and capacity planning.
Implement data quality, observability, and lineage frameworks to ensure trusted data across multiple domains (e.g., sales, inventory, customer loyalty).
Partner with analytics engineers, BI developers, and business stakeholders to deliver well-structured, analytics-ready datasets into the warehouse and lakehouse layers.
Establish and champion engineering best practices, including version control, CI/CD, automated testing, and infrastructure-as-code.
Contribute to platform architecture decisions, tooling evaluations, and documentation that raise the engineering bar across the team.