Technical Lead

Job Title: Technical Lead Job Description

As a Technical Lead , you will play a pivotal technical leadership role in shaping and delivering the modern data foundation that powers digital products across the Orthopaedics Supply Chain. You will help design scalable lakehouse architecture using Databricks, AWS, and modern cloud technologies, while enabling governed, reusable, and business-ready data products for analytics, reporting, AI, and operational decision-making.

This is a role for an experienced professional with deep domain knowledge who can lead across solution architecture, data pipeline design, engineering standards, vendor coordination, and cross-functional execution. You will partner closely with Product Owners, business stakeholders, data engineers, AI/ML teams, and external delivery partners to translate business pain points into scalable technical solutions and executable delivery plans.

You will also help drive modern engineering practices across the team, including AI-assisted development, reusable pipeline patterns, data quality automation, observability, and responsible use of emerging Databricks capabilities. If you are energized by hands-on architecture, solving complex data challenges, and helping teams deliver trusted data products at scale, this is an opportunity to make a meaningful impact.

Key Responsibilities:

• Provide senior technical leadership for the Data Foundation initiative to modernize the enterprise data ecosystem through scalable Lakehouse architecture and cloud-based data platform capabilities
• Design and solution complex data pipelines, integration patterns, and data architecture capabilities using Databricks, AWS, Python, SQL, Spark, Delta Lake, and related cloud-native services
• Translate complex business requirements, product needs, and technical challenges into scalable architecture decisions, implementation plans, and reusable engineering patterns
• Develop solution blueprints for batch, streaming, and event-driven data pipelines that support analytics, business intelligence, AI/ML, and digital product use cases
• Partner with Product Owners, Supply Chain business stakeholders, data governance teams, AI/ML teams, and IT partners to understand pain points, negotiate scope, and align technical delivery with measurable business outcomes
• Lead architecture and design discussions with internal teams and contractors, ensuring solutions meet standards for scalability, reliability, quality, security, maintainability, and cost efficiency
• Break down large technical initiatives into smaller work packages, user stories, engineering tasks, and acceptance criteria that can be executed by internal engineers and external contractors
• Coordinate contractor and vendor execution by assigning work packages, reviewing deliverables, providing technical direction, and ensuring performance against expected outcomes
• Establish and promote best practices for data modeling, pipeline design, modularity, reuse, automated testing, CI/CD, data quality, observability, lineage, monitoring, and operational support
• Lead hands-on development and technical reviews using Databricks, PySpark, Spark SQL, Python, SQL, dbt, Airflow or equivalent orchestration tools, and cloud-native data engineering services
• Champion responsible adoption of AI-assisted engineering practices to improve developer productivity, pipeline quality, documentation, testing, troubleshooting, and delivery velocity
• Mentor engineers and contractors through design reviews, code reviews, architecture guidance, and practical implementation support without direct people-management responsibility
• Contribute to technical governance forums, architecture standards, pipeline frameworks, and operating model practices that improve consistency and reuse across the data engineering organization

Experience and Skills:

Required:

• Minimum of 5 years' experience for this level to be consistent with experience in data engineering, data architecture, platform engineering, or related technical disciplines
• Strong hands-on technical experience with Databricks, Apache Spark, PySpark, Spark SQL, Python, SQL, and Delta Lake
• Strong experience with AWS cloud data services; Azure experience is desirable
• Proven expertise in modern data architecture, lakehouse architecture, enterprise data platforms, dimensional modeling, and scalable data pipeline design
• Experience designing and implementing batch, streaming, and event-driven data processing solutions for enterprise-scale workloads
• Experience with CI/CD pipelines, Git-based development, automation frameworks, testing practices, deployment patterns, and production support processes
• Required experience designing and implementing automated testing strategies for data engineering pipelines, including end-to-end validation, data quality checks, and integration with CI/CD pipelines
• Strong ability to translate business requirements and product needs into technical architecture, user stories, work packages, and executable delivery plans
• Demonstrated ability to coordinate distributed engineering teams, contractors, and external vendors to deliver high-quality technical outcomes
• Excellent communication, facilitation, stakeholder management, and influencing skills across technical, product, and business audiences
• Knowledge of Power BI, Tableau, or enterprise BI platforms
• Knowledge of Workflow Automation tools – examples could include Power Automate, Blue Prism, UiPath or Amazon Quick Flows

Preferred:

• Experience supporting Agile product delivery teams and mentoring engineers in complex, fast-paced technical environments
• Familiarity with end-to-end supply chain domains such as planning, manufacturing, procurement, distribution, logistics, and operations within MedTech or a similar regulated industry
• Knowledge of enterprise systems of record such as SAP, ERP, MES, PLM, and CRM, including experience working with fragmented or legacy data environments
• Experience with Lakehouse architecture, Delta Lake, Unity Catalog, data governance, lineage, access controls, metadata management, and data observability frameworks
• Experience driving engineering teams toward responsible use of AI-assisted development tools such as GitHub Copilot, Claude Code, Amazon Q Developer, Cursor, Databricks Assistant, Databricks Genie, Genie Code, or similar coding and data engineering copilots
• Experience applying AI-assisted engineering practices to accelerate pipeline development, generate or refactor code, create automated unit tests, draft documentation, analyze failures, troubleshoot data quality issues, and improve developer productivity
• Familiarity with modern Databricks capabilities such as Lakeflow, Lakeflow Designer, Lakeflow Jobs, Spark Declarative Pipelines, Lakeflow Connect, Genie Code for agentic pipeline development, and AI/BI Genie for natural-language data interaction
• Familiarity with Databricks Mosaic AI capabilities, including model serving, AI application development, AI Search, vector indexes, MLflow evaluation, MLflow tracing, AI observability, and LLMOps practices
• Experience designing AI-ready data foundations that support semantic layers, business ontologies, metadata-driven architectures, knowledge graphs, governed data products, and natural-language consumption patterns
• Experience building or enabling conversational data experiences, chatbots, or agentic workflows that help business users interact with governed enterprise data using natural language
• Experience with dbt, Airflow, Terraform, Databricks Asset Bundles, GitHub Actions, or equivalent modern data engineering and infrastructure-as-code tooling
• Experience collaborating with AI/ML and advanced analytics teams to enable scalable, trusted, and model-ready data pipelines

Leadership Competencies

• Strong technical leadership with the ability to influence without relying on formal authority
• Ability to lead effectively in an outcome-driven, product-centric operating model
• Excellent communication, stakeholder management, negotiation, and influencing skills across technical and business audiences
• Proven ability to translate complex architectures into executable delivery plans and manageable work packages
• Strong ownership mindset with a focus on standardization, scalability, engineering excellence, and measurable business impact
• Ability to balance hands-on engineering depth with architecture guidance, vendor coordination, and cross-functional delivery leadership