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Uzņēmums šīs atziņas nodrošina, tostarp ar S&P Global Market Intelligence, S&P Global Ratings un S&P Dow Jones Indices starpniecību. Tā datu vadītie produkti atbalsta izpēti, riska analīzi, salīdzinošo novērtēšanu un stratēģisko plānošanu sarežģītos globālajos tirgos.
Director of Software Engineering, Data Platforms
Lead Databricks platform transitions, AI-assisted engineering, and enterprise data mastering integration. Guide offshore teams and shape data platform initiatives at S&P Global.
Apraksts
- Coordinate with the core Databricks team to move existing data pipelines onto the target enterprise platform
- Create repeatable patterns for dataset ingestion, transformation, testing, deployment, and monitoring
- Drive adoption of AI development tools, including Claude, GitHub Copilot, and large language models
- Build context engineering practices and reusable context packs
- Help offshore teams define and put into practice onboarding patterns that work across data assets, domains, and source systems
- Create reusable ingestion and processing approaches for batch and streaming workloads
- Connect platform pipelines and datasets to the enterprise data mastering platform
- Put data quality checks, reconciliation processes, and metadata alignment in place
- Promote consistent business definitions, reusable data concepts, and governed data use
- Act as a senior technical authority in offshore data platform engineering
- Shape architecture decisions and advise engineers and partner teams
- Take part in design and code reviews for complex data platform work
Prasības
- Bring at least 10 years of experience in data engineering, data platforms, cloud data architecture, software engineering, or a related engineering field
- Have worked as a senior individual contributor, technical lead, principal or staff engineer, distinguished engineer, architect, or in an equivalent role
- Demonstrate strong hands-on skills in modern data engineering and pipeline development
- Have experience with batch or streaming data workflows
- Have experience with Databricks platforms and lakehouse architectures
- Have practical experience with AI-assisted engineering tools such as Claude, GitHub Copilot, large language models, or similar tools
- Have applied large language models to engineering tasks such as code generation and optimization
- Attend at least one in-person interview if an external candidate advancing to the final interview stage
- Be familiar with enterprise data catalogs, metadata management, business glossaries, lineage, operational metadata platforms, or semantic data layers
- Have experience with NeoXam DataHub or similar data mastering workflows
- Understand semantic modeling
- Show strong leadership through coaching, empowering others, and building consensus
Priekšrocības
- Eligible for an annual incentive plan
- Health care coverage
- Generous paid time off
- Ongoing learning and career development resources
- Retirement planning
- Continuing education program
- Company-matched student loan contributions
- Financial wellness programs
- Family benefits and perks
- Retail discounts
- Referral incentive awards