Data Engineer - Databricks

Role Overview:

Teqfocus is looking for an experienced Data Engineer to support an enterprise Software Realization initiative focused on building a unified data foundation using Databricks.

The role involves integrating data from multiple enterprise sources, including sourcing and Asset Registry data, into a Unified Data Layer (UDL). The successful candidate will be responsible for developing reliable data ingestion pipelines, transforming and reconciling datasets, implementing data quality checks, and preparing curated datasets to support analytics, reporting, and AI-driven intelligence capabilities.

This is a hands-on, deliverables-driven contract engagement requiring strong expertise in Databricks, SQL, data integration, and enterprise data engineering.

About Teqfocus:

Teqfocus is a Data and AI company that helps enterprises build and scale the Agentic Enterprise — connecting the data foundation, the intelligence layer, and the applications teams run their businesses on into a single, coherent architecture.

As a Salesforce Summit Partner, we work in Healthcare, Life Sciences, Financial Services, Insurance, and Hi-Tech — industries where getting AI right matters and the tolerance for failure is low. Our delivery model is senior-heavy by design. The same team that scopes an engagement ships it to production. There are no junior shadow teams, no sub-contractors brought in when the problem gets hard, and no hand-off to a separate managed services vendor when the project closes.

We run an internal innovation practice that builds the reusable patterns, pre-tested architectures, and industry-specific frameworks that give every Teqfocus client a head start — and give every Teqfocus practitioner work that compounds over time.

We are a diverse, immigrant-entrepreneurial company. We were built on the conviction that the best outcomes come from people who take genuine ownership of what they build — and are trusted to do so.

www.teqfocus.com

Key Responsibilities:

• Design, develop, and maintain data ingestion pipelines to bring data from multiple enterprise sources into the Unified Data Layer (UDL).
• Integrate data from sourcing systems and Asset Registry sources, ensuring accurate and consistent data consolidation.
• Build and maintain scalable ETL/ELT pipelines using Databricks and SQL.
• Develop data transformation and mapping logic to combine contract, application, and business capability datasets.
• Implement data reconciliation mechanisms to establish relationships between sourcing contracts and application records using unique identifiers.
• Perform data validation, cleansing, and quality checks to identify missing, inconsistent, or incomplete records.
• Implement automated data refresh processes, including daily data updates and refresh monitoring.
• Develop curated and structured datasets to support downstream analytics, reporting, and dashboard development.
• Support data preparation for intelligence capabilities, including datasets used to identify similar software tools and overlapping business capabilities.
• Collaborate with business stakeholders, data architects, and analytics teams to understand data requirements and translate them into technical solutions.
• Troubleshoot data pipeline issues and optimize performance, reliability, and scalability.

Required Qualifications:

• 5+ years hands-on experience in Data Engineering, preferably with enterprise data integration projects.
• Proficiency in Databricks and SQL.
• Experience building and managing ETL/ELT pipelines and data transformation workflows.
• Strong understanding of data modelling, data mapping, and data integration concepts.
• Experience working with structured and semi-structured data.
• Hands-on experience with data validation, data quality checks, and reconciliation.
• Experience integrating data from multiple enterprise systems and external data sources.
• Understanding of data warehousing, curated data layers, and modern data architecture.
• Strong analytical and problem-solving skills with the ability to work independently in a deliverables-driven environment.

Good to Have:

• Experience with procurement, sourcing, or contract management datasets.
• Experience integrating data from Google Sheets or spreadsheet-based enterprise data repositories.
• Familiarity with Asset Registry, application portfolio management, or software asset management.
• Experience preparing datasets for AI/LLM-based intelligence or similarity-matching solutions.
• Familiarity with Databricks SQL, Delta Lake, and data governance practices.
• Experience supporting business intelligence dashboards and reporting solutions.