
Sr. Databricks Engineer
Austin, TXJobSeen 2 days agoSeen in employer's feed 2 days ago
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At a glance
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Requirements
Credentials this posting asks for.
Job overview
The Senior Data Platform Engineer designs, develops, and supports enterprise cloud data platform capabilities, with a focus on Azure Databricks, cloud data engineering, modernization, automation, and governance. The role balances new capability development with operations and reliability, and serves as a technical lead on major initiatives while remaining accountable for production support. It also builds and operationalizes AI agent solutions using governed enterprise data, models, tools, and APIs.
Skills & qualifications
Skills
Qualifications
Full job description
Job Description
The Senior Data Platform Engineer is a senior individual contributor responsible for designing, developing, enhancing, and supporting enterprise cloud data platform capabilities. Reporting to the Sr. Director of Data Platform Transformations & Operations, this role combines hands-on engineering, solution design, and operational ownership to deliver scalable, secure, and reliable solutions. The engineer serves as a technical lead on major initiatives while remaining accountable for production support and operational excellence.
The role supports platform transformation and steady-state operations, with approximately 70% focused on development of new capabilities and 30% on operations, support, and platform reliability. Primary areas include Azure Databricks, cloud data engineering, platform modernization, automation, governance, and continuous improvement.
Responsibilities
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Design, develop, and maintain scalable data pipelines, ingestion frameworks, transformation processes, and reusable data products using Azure Databricks, PySpark, SQL, and Delta Lake.
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Implement Bronze, Silver, and Gold architecture patterns supporting enterprise reporting, analytics, AI, and self-service data consumption.
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Design, build, and operationalize AI agent solutions on the Databricks platform, integrating governed enterprise data, models, tools, and APIs for secure and scalable business use cases.
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Build reusable frameworks, utilities, and platform components that improve engineering productivity, quality, consistency, and deployment speed.
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Develop and support batch, near-real-time, and streaming integration solutions, including modernization of legacy warehouse and ETL workloads.
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Serve as technical lead for complex initiatives; develop solution designs, lead technical reviews, recommend tools and approaches, and guide work through production implementation.
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Partner with data architects and platform leaders to ensure solutions are scalable, secure, governed, cost-conscious, and operationally supportable.
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Mentor engineers and promote standards for coding, testing, documentation, performance, and production readiness.
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Implement data quality, validation, reconciliation, monitoring, metadata, and lineage capabilities; support RBAC and enterprise security controls.
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Build and maintain CI/CD, automated testing, deployment, and release processes using Azure DevOps and Git-based practices.
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Contribute to platform observability, alerting, operational dashboards, health metrics, performance tuning, and cost optimization.
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Participate in production support, incident response, pager, and on-call rotations; troubleshoot issues, lead root cause analysis, and implement durable remediation.
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Create and maintain operational runbooks, support procedures, technical documentation, and knowledge-sharing assets.
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Collaborate with architecture, governance, security, analytics, application, and business teams to translate requirements into scalable platform solutions.
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Support technical discovery, estimation, roadmap planning, delivery execution, and evaluation of emerging cloud, data, and AI capabilities.
Skills and Requirements
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Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field.
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Master's degree preferred.
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10+ years of experience in data engineering, data warehousing, or data platform engineering.
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5+ years of experience designing or implementing cloud-based data warehouse, data lake, or data platform solutions.
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3+ years of hands-on Azure Databricks experience in enterprise production environments.
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Experience with ETL/ELT development, data modeling, large-scale integration, and cloud platform modernization.
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Experience leading technical implementations, solution design, design reviews, and production releases.
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Experience supporting production environments, incident response, root cause analysis, and operational support processes.
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Experience in regulated, financial services, banking, or audit-sensitive environments preferred.
Skills & Competencies
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Advanced proficiency in SQL, Python, PySpark, and Spark performance optimization.
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Strong experience with Azure Databricks, Delta Lake, Unity Catalog, Databricks Workflows, and Delta Live Tables.
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Hands-on familiarity with Databricks agentic AI capabilities, including Agent Bricks or Agent Framework, MLflow tracing and evaluation, AI Search or Vector Search, Unity Catalog governance, and production deployment of RAG or tool-using agents.
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Strong understanding of lakehouse, Medallion Architecture, dimensional modeling, data warehousing, and analytics-oriented data structures.
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Experience with Azure DevOps, Git, CI/CD, automated testing, and deployment practices; Databricks Asset Bundles experience preferred.
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Experience implementing data quality, validation, reconciliation, monitoring, metadata, lineage, governance, and security controls.
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Strong technical leadership, solution design, analytical, troubleshooting, and problem-solving skills.
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Ability to independently lead complex work from design through production support and balance innovation with reliability, security, cost, and supportability.
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Excellent collaboration, mentoring, documentation, and communication skills across technical and business teams.
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Experience with Power BI, MicroStrategy, MDM, streaming architectures, infrastructure automation, or AI-assisted development preferred.
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Databricks Data Engineer certification at the Associate or Professional level preferred. Financial services industry exp Deployment of genAI projects Data governance
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal employment opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment without regard to race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request to [email protected].
How this job compares
Set against the 460 open Data Platform Engineer jobs on Olive Jobs, counted November 2, 2026.
- Pay
- This postingNot listed
- US median$130–235K/yr390 salary ranges
- Openings
- Olive Jobs lists 27 open Data Platform Engineer jobs in Texas and 460 across the US. 21% of them nationwide are listed as remote or flexible.
- Hiring the most in Texas
- Accenture3 open
- Academy Sports + Outdoors2 open
- Capital One2 open
- Experience
- This posting asks for at least 10 years of experience — more than three in four Data Platform Engineer postings that state a minimum ask for (median 6 years, across 379 postings).
- Education
- This posting asks for a bachelor's degree; 84 of the 460 open Data Platform Engineer jobs (18%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 2, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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