Senior Databricks Engineer

Who We Are

HIKE2 is an advisory and innovation partner helping organizations design and build what’s next.

We work with complex and regulated industries, including law firms, financial services, insurance, professional services, and high-growth SaaS companies, to modernize operating models and embed data, automation, and AI into the core of how work gets done.

Our clients aren’t just upgrading technology. They are rethinking workflows, activating governed AI, and designing environments where humans and intelligent systems work side by side.

We believe meaningful transformation happens when strategy, data, design, and engineering move together. Siloed initiatives don’t scale. Real progress requires clarity of vision, strong data foundations, responsible governance, and disciplined execution.

Our teams bring deep industry expertise, human-centered design, and advanced data and cloud capabilities to architect secure, scalable solutions built for real-world complexity. From modern data platforms to AI-enabled workflows and enterprise automation, we help organizations move from experimentation to measurable impact.

We thrive in change and move from blank slate to working systems. We care deeply about outcomes, trust, and building long-term partnerships.

We don’t just implement technology. We help design the future of work.

HIKE2 is looking for a Senior Data Engineer with deep experience in Databricks and modern data platforms, particularly in large, complex enterprise environments. This is a hands-on role for someone who has built and delivered data solutions at scale, ideally in greenfield settings, and is comfortable working with Fortune 500 clients. You’ll be expected to lead technical direction, make sound architectural decisions, and support the growth of other engineers on the team.

You’ll work closely with clients and internal teams to design and implement enterprise-grade data platforms and pipelines from the ground up. This role requires someone who can operate across the full lifecycle from early architecture and design through delivery and optimization, while keeping solutions practical, reliable, and aligned with business needs.

What You’ll Do

• Design and build large-scale data platforms on Databricks (Delta Lake, Spark, Unity Catalog) in Azure

• Develop and maintain batch and streaming data pipelines for high-volume, complex data sources

• Implement medallion/lakehouse architectures from the ground up in greenfield environments

• Build and optimize data models to support analytics, reporting, and downstream applications

• Integrate Databricks with enterprise systems (APIs, event streams, warehouses, ML workflows)

• Tune Spark jobs and pipelines for performance, reliability, and cost at scale

• Support production deployments, including CI/CD pipelines, testing, and release management

How You’ll Work

• Partner directly with enterprise clients to translate requirements into working technical solutions

• Collaborate with architects, engineers, and data scientists across multiple workstreams

• Balance speed and quality, knowing when to move fast and when to harden solutions

• Make pragmatic decisions in ambiguous, evolving environments (especially greenfield builds)

• Contribute hands-on while also guiding design and approach across the team

• Communicate tradeoffs clearly to both technical and non-technical stakeholders

• Work within modern engineering practices (version control, code reviews, automated testing)

• Demonstrated ability to mentor and guide data engineers and analysts

What You’ll Own

• End-to-end delivery of Databricks-based data solutions—from design through production support

• Technical direction and key architecture decisions for large-scale implementations

• Data pipeline reliability, monitoring, and incident response in production environments

• Performance and cost efficiency of workloads running in Databricks and Azure

• Data quality, governance alignment, and adherence to enterprise security standards

• Reusable patterns, frameworks, and standards for scaling future implementations

• Mentorship and technical development of other engineers on the team