Senior Consultant – Modern Data & Analytics Engineering

Torq•Plano, TX

About The Position

Torq is looking for experienced data engineers and architects who combine strong technical skills with consulting judgment and a focus on business outcomes. At Torq, our data engineers are not limited to one layer of the technology stack. You will help clients navigate the full data lifecycle - from source-system analysis and data acquisition through integration, modeling, governance, quality, analytics, and production support. You will design and build modern data platforms that enable better reporting, faster decisions, operational efficiency, advanced analytics, and AI. This role is ideal for someone who is comfortable getting hands-on with technology, leading technical workstreams, and explaining complex architecture decisions to both technical and business stakeholders.

Requirements

Ideally 5+ years of experience in data engineering, data architecture, analytics engineering, cloud data platforms, or related technical and analytical work.
Experience working in consulting, professional services, or another client-facing delivery environment is strongly preferred.
Strong problem-solving skills and the ability to break ambiguous business and technical challenges into practical solutions.
Ability to lead a technical workstream, manage priorities and dependencies, and deliver high-quality work with limited oversight.
Strong written and verbal communication skills, including the ability to explain complex technical concepts in business-friendly language.
Experience collaborating with data consumers, business stakeholders, application teams, infrastructure teams, security teams, and executive sponsors.
Ability to balance technical quality, delivery timelines, client priorities, and long-term maintainability.
A four-year degree in Computer Science, Data Science, Information Systems, Engineering, or a related field, or equivalent professional experience.
Advanced SQL skills and experience designing efficient solutions for large and complex datasets.
Experience with Python, Spark, Java, Scala, or another language commonly used for data engineering.
Hands-on experience designing and developing ETL or ELT pipelines using tools such as dbt, Apache Airflow, Azure Data Factory, Fabric Data Factory, Databricks Workflows, AWS Glue, or comparable technologies.
Experience with one or more major cloud environments: Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
Experience with modern data platforms such as Microsoft Fabric, Databricks, Snowflake, Synapse Analytics, BigQuery, or Redshift.
Strong knowledge of dimensional modeling, normalized modeling, semantic models, data vault, medallion architectures, and related data-design patterns.
Experience acquiring data through APIs, files, relational databases, SaaS platforms, event streams, and other enterprise sources.
Understanding of data security, access control, privacy, governance, metadata, lineage, and lifecycle-management concepts.
Experience applying Git, automated testing, infrastructure as code, deployment pipelines, and CI/CD practices to data solutions.
Familiarity with reporting and analytics platforms such as Power BI, Tableau, Looker, or comparable tools.

Nice To Haves

Microsoft Fabric and OneLake
Azure Databricks or Databricks on AWS
Snowflake and Snowpark
Delta Lake and lakehouse architecture
Kafka or other event-streaming technologies
Terraform or other infrastructure-as-code tools
Microsoft Purview, Unity Catalog, Collibra, or Informatica
Data mesh and data-product operating models
Master data management and reference data management
Data observability, automated data quality, and platform monitoring
Semantic modeling and advanced Power BI development
Machine-learning and generative-AI data pipelines
Vector databases, unstructured-data processing, retrieval-augmented generation, and AI-ready data architecture
Experience in automotive, airline, insurance, financial services, energy, healthcare, or consumer services

Responsibilities

Lead technical workstreams across data platform modernization, cloud migration, enterprise integration, analytics, and AI-enablement initiatives.
Assess existing data ecosystems, identify architectural and engineering constraints, and define practical modernization recommendations.
Design scalable cloud-based data architectures, including data lake, lakehouse, data mesh, and data-product patterns.
Build and optimize batch, near-real-time, streaming, and API-based data integrations across enterprise source systems.
Develop reliable ETL and ELT pipelines using modern engineering frameworks, orchestration tools, and cloud-native services.
Design conceptual, logical, and physical data models that support operational reporting, business intelligence, analytics, and AI use cases.
Partner with business analysts, product owners, data consumers, and subject-matter experts to translate requirements into durable technical solutions.
Establish data quality controls, reconciliation processes, monitoring, observability, and operational support procedures.
Implement metadata, lineage, security, privacy, and governance requirements as part of the engineering lifecycle.
Apply software engineering practices - including version control, automated testing, code review, documentation, and CI/CD - to data solutions.
Evaluate technology options, document architectural decisions, and communicate tradeoffs clearly to client stakeholders.
Troubleshoot complex data issues across source systems, pipelines, models, platforms, and reporting layers.
Guide junior consultants and engineers by reviewing work, sharing context, and improving the quality and consistency of technical delivery.
Contribute to Torq accelerators, reusable frameworks, reference architectures, engineering standards, and technical enablement.
Support business development by identifying client needs, shaping technical solutions, estimating delivery efforts, and contributing to proposals and presentations.