Consultant, Data Engineering

Consultant, Data Engineering
Data Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods, and models. What’s more, we are in collaboration with leading academics, industry experts, and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.
What you’ll achieve:
As a Consultant, you will lead high-impact data engineering projects that strengthen ingestion, transformation, and data product delivery. You will improve observability and quality, drive standards adoption, and coordinate readiness for analytics and AI workloads. You will collaborate with stakeholders to deliver scalable solutions, robust documentation, and measurable value.
Build the data foundation that grounds AI agents on facts they can trust. You turn raw, messy, multi-source data - structured feeds and unstructured content alike - into canonical, provenance-tagged, SLA-fresh Gold data that the Knowledge & Intelligence layer serves. Working with business leaders and internal customers, you set the design standards and assurance processes that keep the pipeline compatible, operable, and traceable end to end. This is hands-on data engineering where correctness and lineage aren't nice-to-haves - they're the product. You develop fundamentally new approaches to pull meaning out of data, and every record you land answers the question “where did this come from, and can we trust it?”
Join us to do the best work of your career and make a profound social impact as a Consultant on our Data Engineering team.
Take the first step towards your dream career
Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:
Essential Requirements
Lakehouse platforms - Databricks, IOMETE, or similar. In-depth Spark - massive-volume data processing and fine-tuning.
PySpark, Java, and Python. Table formats - in-depth knowledge of Iceberg, Delta Lake, or similar.
Advanced querying on RDBMS - complex data retrieval across Teradata, Oracle, SingleStore, and SQL Server.
Deep understanding of data integrity and consistency. Workflow frameworks - Airflow, AWS Step Functions, or similar.
Chunking, embeddings, and vector databases. S3-compatible storage.
Desirable Requirements
Bachelor’s degree in Computer Science, Data Engineering, or related field; advanced degree preferred.
Mongo DB
You will:
The medallion pipeline. Take landed data through Bronze → Silver → Gold — conforming structured sources and extracted content to Gold within its freshness SLA, with traceable lineage at every hop.
Entity resolution. Own the resolve gate that turns many sources into one canonical identity — the join everything downstream depends on.
Model-based extraction lanes. Build the NLP- and LLM-assisted lanes that pull structure out of unstructured content across many content types — HTML, PDF, DAM assets, and more — tagged as evidence, not fact.
Provenance and trust. Tag provenance and trust tier on every record. Quarantine what fails the gate with its reason attached, rather than silently dropping it.
Performance at scale. Keep high-volume data in the lakehouse — and the served layer — lean and fast.

Work arrangement
No