Data Engineer
Confirmed on the employer's own hiring board on Oct 14, 2026. First seen by Alion on Sep 18, 2026.
ABOUT SOVRA
SOVRA is a leading public procurement platform trusted by more than 7,000 government agencies and over 1 million suppliers across North America. Our work sits at the intersection of technology, public service, and accountability, helping governments operate more efficiently and transparently on behalf of the communities they serve.
What makes SOVRA unique is our deep focus on the public sector. Our solutions are purpose-built to solve real, complex procurement challenges, balancing compliance with usability and innovation. That commitment has been recognized with the Achievement of Excellence in Procurement (AEP) Certification from the National Procurement Institute, reflecting our high standards and impact in the market.
At SOVRA, the work you do matters. Every improvement we make helps public organizations stretch taxpayer dollars further, operate with greater transparency, and deliver better outcomes for millions of people. We’re a growing, mission-driven company where smart, curious people come together to build technology that serves the public good.
Learn more at sovra.com
THE ENVIRONMENT
SOVRA runs a lakehouse platform (Bronze / Silver / Gold layers on Apache Iceberg), with AWS DMS and Zero-ETL for ingestion, dbt for transformation, and AWS-native services (S3, Glue, Athena, Bedrock) for storage, compute, and AI enablement. The Data Engineering Specialist works inside this stack, embedded in the Data Platform team.
What we're looking for. A hands-on data engineer fluent in SQL, Python, dbt, and modern lakehouse patterns on AWS, comfortable owning pipelines end-to-end - from source-system CDC through governed Gold models and used to working in a governed, multi-consumer environment where data is treated as a product, not a report.
Daily rhythm. A short standup with the Data Platform team to review pipeline health, ingestion status, and delivery blockers. Working sessions with data analysts on Gold-layer model validation, and pairing with fellow data engineers on shared transformation logic.
Core engineering work (majority of the week). Building and maintaining ingestion pipelines from source systems (product databases, and eventually SaaS platforms) into the Bronze layer; developing Silver-layer cleansing and conforming logic; and modeling Gold-layer datasets in dbt for consumption by internal teams and external customers.
Consumer enablement. Partnering with internal product, GTM, and AI stakeholders to translate use cases into Gold-layer data products, and supporting external customer needs (direct data access, embedded BI, curated exports) through the platform's productized surfaces.
Reliability and governance. Monitoring pipeline SLAs, resolving production incidents, maintaining catalog and lineage metadata, and running data-quality checks. Occasional bounded one-time work - regulatory data requests, audit evidence, incident investigations.
Platform evolution. Contributing to new ingestion patterns (e.g. API-based SaaS connectors, Data streams, etc...), AI-consumable dataset design, and cost/performance tuning of the lakehouse.
MAIN RESPONSIBILITIES:
Collaborate with cross-functional teams to gather data requirements.
Design, develop, and maintain scalable data pipelines to process and integrate data from various sources.
Optimize data pipelines for performance, cost efficiency, and data quality.
Design and implement data models and schemas that meet business requirements.
Develop and maintain logical and physical data models to support data warehousing and reporting.
Ensure data consistency and integrity across different data storage systems.
Design, develop, and maintain ETL processes to extract, transform, and load data from multiple sources.
Monitor data integration processes, troubleshoot, and resolve any issues that may arise.
Collaborate with data source owners to ensure the availability and quality of the data.
Implement data quality controls and validation processes to ensure data accuracy and reliability.
Collaborate with data analysts, and other stakeholders to ensure data availability and accessibility for analytical purposes.
Provide technical support and expertise in data engineering and related tools and technologies.
Continuously improve data engineering practices and stay up-to-date with industry trends and best practices.
PROFILE:
Strong problem-solving, analytical, and communication skills.
Detail oriented
Ability to work independently and as part of a team in a fast-paced environment;
Good interpersonal and communication skills and focus on customer satisfaction
QUALIFICATIONS:
Bachelor's degree in computer science, Engineering, or a related field.
Three 3-10+ in data engineering, data warehousing, and ETL processes.
Proficiency in programming languages such as Python and SQL
Experience with AWS data services, particularly S3, Glue, and Athena
Hands-on experience with dbt (data build tool) for data transformation and modeling
Knowledge of data lakehouse patterns and medallion architecture (bronze/silver/gold layers)
Experience with Infrastructure as Code (IaC) tools, specifically Terraform
Experience with relational and NoSQL databases
Understanding of AI and machine learning concepts, with the ability to prepare and structure data to support AI-driven products
Fluent in English and French, both verbally and written
Authorized to work in Canada-unfortunately we are not able to sponsor work visas or transfers at this time.
Thank you for your interest in SOVRA.
At SOVRA, we are committed to fostering an inclusive and equitable workplace. We are an equal opportunity employer and do not discriminate against any employee or applicant for employment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, marital status, veteran status, or any other characteristic protected by applicable laws. We provide a work environment free from discrimination and harassment. In addition, we are committed to ensuring pay equity across our organization and regularly review our compensation practices.