Hello,
I have below exclusive position with my client. Please let me know your interest so that we can move ahead for further steps.
Job Title: Senior Data Engineer
Location: 100% Remote
Job Type: 06+ Months Contract
Job Summary:
About the Role
We are building a data world where value moves the way information does today. Our data platform is being built on Databricks, and it will power the next generation of the treasury management SaaS user experience through embedded Omni analytics.
We are seeking an innovative, forward-thinking Data Engineer (Contract) who is energized by building a world-class platform alongside a world-class team. In this role you will collaborate with the team and help set the technical direction of the platform and raise the bar on engineering excellence — turning a complex, multi-tenant data estate into a product our customers rely on every day.
This is a hands-on staff-level role. You will spend your time in architecture and design, in code, and in the room where technical decisions get made.
What You''ll Do
Architecture & Design
• Adopt a solution mindset for the Platform Data team by building a robust, scalable, and resilient data platform that serves every application in the portfolio.
• Drive design consistency across multi-functional project teams by evaluating solution architecture options and guiding teams to the right one.
• Influence at the concept stage. Engage early in concept discussions and proposed technology solutions so that business objectives shape the architecture rather than working around it.
• Raise engineering standards through design reviews, code reviews, and mentorship of engineers across the data community.
Platform & Pipeline Engineering
• Stay hands-on. Dive into code with the team and ship data products end to end — not just diagrams and decks.
• Design and build ELT/ETL at scale on Databricks, applying medallion (Bronze/Silver/Gold) architecture with well-defined contracts between layers.
• Build production-grade pipelines using Delta Lake, Delta Live Tables, Auto Loader, Structured Streaming, and Databricks Workflows, with checkpointing and schema evolution handled deliberately.
• Model data for consumption. Design relational, dimensional, and star-schema models that serve both analytical workloads and low-latency, in-product analytics.
• Engineer for cost and performance. Tune cluster configuration, job design, table layout, and compute choices; own Delta table maintenance (OPTIMIZE, Z-ORDER/liquid clustering, VACUUM) and platform usage cost.
• Automate everything repeatable — CI/CD for notebooks, jobs, and Databricks Asset Bundles; infrastructure as code; environment promotion; automated testing of data and pipelines.
Governance, Quality & Reliability
• Own governance in Unity Catalog — catalogs, schemas, access control, lineage, audit logging, and masking or tokenization of sensitive fields.
• Build data quality in, not on. Implement expectations, validation, anomaly detection, and observability so issues are caught before consumers see them.
• Ensure all work meets the quality, operational, security, and architectural standards the organization depends on, including the controls expected of a financial services environment.
• Run what you build. Participate fully in the team''s YBIYRI (you-build-it-you-run-it) model, including the on-call pager rotation, and drive the operational improvements that reduce toil.
Collaboration & Ways of Working
• Partner with solution teams, product managers, and business stakeholders to deliver cloud-based data solutions that meet real needs.
• Work in an agile, analytical way every day, and continuously improve the team''s operations, processes, methodologies, technology choices, and practices.
• Share information across boundaries. Interact with your team, customers, and peers to improve cross-department processes.
• Champion transparency and partnership between engineering and our product partners.
• Stay ahead of the curve. Track technology trends, experiment with new tools, and participate in internal and external technical communities.
Who You Are
You lead with data architecture judgment first, and you back it with depth in distributed data engineering. You are comfortable owning a technical domain outright, and equally comfortable pairing with an engineer to debug a Spark job.
Experience & Approach
• Data architecture acumen, first and foremost — you can reason about a data estate holistically, not just pipeline by pipeline.
• Deep data engineering experience in complex distributed systems, typically 15+ years in data engineering with multiple years designing and operating large-scale platforms in production.
• Strong technical acumen that allows you to be accountable for a technical domain and the decisions made within it.
• Strong command of the software development lifecycle and engineering excellence practices — version control, testing, code review, CI/CD, observability — applied to data, not just applications.
• Proven ability to manage moderately sized feature builds end to end, from ambiguous problem statement to operating system in production.
• Strong communication, coordination, and influencing skills, including the ability to explain technical trade-offs to non-technical partners and to build alignment without formal authority.
• A track record of collaboration and elevating the teams around you.
Technical Skills
• Databricks, expert level — Delta Lake and Delta tables, Delta table maintenance, Unity Catalog, access control, compute configuration, and usage cost management.
• Automation of ETL/ELT and data movement using medallion architecture and Databricks-native frameworks. Qlik replicate or any other CDC tools.
• Languages and frameworks: advanced SQL and Python (PySpark); PowerShell.
• Databases and storage: SQL Server, PostgreSQL, and data lake / lakehouse architectures.
• Cloud: Azure PaaS services (for example Data Lake Storage, Data Factory, Key Vault, Functions, Event Hubs) and the networking, identity, and secrets management patterns around them.
• Data modeling: relational, star-schema and dimensional modeling, and ER diagramming.
• BI and analytics tooling: Omni Analytics, Power BI, Tableau, Looker, or similar. Databricks Genie Spaces will be nice to have.
• Orchestration, CI/CD, and infrastructure as code — ADO, Octopus deploy or similar, Databricks Workflows or equivalent, Git-based workflows, and Terraform or comparable tooling.
Bonus Points
• Exposure to Omni analytics and embedded/in-product analytics delivery.
• Experience with SSRS and migrating legacy reporting estates to a modern lakehouse.
• Background in fintech, treasury management, payments, or another regulated financial domain.
• Experience with multi-tenant data isolation and row/column-level security in a SaaS product.
• Experience with streaming and near-real-time analytics serving customer-facing workloads.
What Success Looks Like
• First 10 days — you understand the current data estate, its consumers, and its pain points, and you have a point of view on the highest-leverage architectural changes.
• First 20 days — you have delivered meaningful platform capability in production and established or strengthened at least one engineering standard the team adopts.
• Beyond — the platform is measurably more reliable, more cost-efficient, and faster to build on, and the engineers around you are operating at a higher level than when you arrived.
How We Work
We are an agile team that practices YBIYRI: the engineers who build a system operate it, including participation in an on-call rotation. We favor transparency over ceremony, strong written design over hallway consensus, and automation over repeated manual effort. We expect engineers to challenge decisions — including ours — with evidence.
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