„Marsh McLennan“ yra pasaulinė profesionalių paslaugų įmonė, teikianti konsultavimo paslaugas rizikos valdymo, strategijos ir su žmonėmis susijusiose srityse. Įmonių grupei priklauso draudimo tarpininkavimo ir rizikos valdymo paslaugas teikianti „Marsh“, perdraudimo ir kapitalo strategijų srityje besispecializuojanti „Guy Carpenter“, sveikatos, turto ir karjeros konsultavimo paslaugas teikianti „Mercer“ bei strategijos, ekonomikos ir prekių ženklų konsultavimo srityse besispecializuojanti „Oliver Wyman“. „Marsh McLennan“ visame pasaulyje dirba daugiau nei 85 000 darbuotojų, o įmonė sutelkia finansų, konsultavimo, rinkodaros ir kitų susijusių profesionalių paslaugų kompetencijas. Įmonės įdarbinimo sritys apima klientų konsultavimą, rizikos analizę, verslo strategiją, konsultavimą ir veiklos komandas palaikančias pareigas visose keturiose įmonėse.
Senior Technical Lead, Data Engineering — Marsh McLennan, Mexico City
Lead hands-on data engineering teams at Marsh McLennan, turning business priorities into reliable data solutions. Build and operate Databricks pipelines across AWS and Azure.
Aprašymas
- Lead a delivery squad of 4–8 data engineers, including employees and contractors.
- Set technical direction, break work into manageable tasks, coordinate sprint execution, and own the squad’s deliverables.
- Shift with the engineers to priority initiatives as projects wrap up or move into steady-state operations.
- Write code, build pipelines, and troubleshoot production issues with SQL, Python, and Spark.
- Design data models, medallion architectures, ingestion contracts, and reusable engineering patterns.
- Manage schema changes, data quality checks, idempotency, and data lineage.
- Partner with Engagement Leads and business stakeholders to turn business problems into data solutions.
- Advance CI/CD, version control, testing, code reviews, documentation, developer experience, and operational practices.
- Run Databricks on AWS and Azure with Unity Catalog, Delta Lake, Spark, infrastructure as code, Git workflows, and pipeline orchestration tools.
Reikalavimai
- At least 5 years of hands-on data engineering experience, including some experience leading or mentoring engineers.
- Strong, recent, regular use of SQL, Python, and Spark.
- Practical experience with cloud data platforms, preferably Databricks; Snowflake, Synapse, or similar platforms are also relevant, on AWS, Azure, or GCP.
- Ability to lead a blended team of employees and contractors by setting expectations, managing performance, and building cohesion.
- Working knowledge of modern engineering practices, including Git, CI/CD, automated testing, infrastructure as code, and DataOps.
- Curiosity about the business domain.
- Clear communication skills to explain technical tradeoffs to business leaders and convey business requirements to engineers.
- Databricks experience with Unity Catalog, Delta Lake, medallion architecture, and Databricks workflows is valued.
- Experience with dbt or a similar transformation framework is valued.
- Familiarity with data governance practices such as lineage, access controls, metadata management, and data quality frameworks is valued.
- Previous work in financial services, insurance, benefits, or consulting is valued.
- Experience building ingestion pipelines for documents or unstructured data is valued.
- Exposure to LLM-based tools, AI-assisted development, or analytics engineering is valued.
Privalumai
- Hybrid work with flexibility to work remotely and collaborate in the office.
- Opportunities for professional development through in-office collaboration.
- A diverse, inclusive, and flexible work environment.