We are seeking a Principal Data Engineer to lead the architecture, design, and delivery of modern data platforms and analytics solutions for clients. This is a hands-on technical leadership role for someone who can move comfortably between executive-level client conversations, solution architecture, engineering delivery, and mentoring high-performing data teams.
The ideal candidate combines deep data-engineering expertise with strong consulting instincts: they can translate business objectives into scalable technical solutions, communicate complex concepts through clear presentations, and guide teams from discovery through implementation and operationalization.
Key Responsibilities- Lead the end-to-end architecture, design, and delivery of enterprise data engineering, data platform, and analytics solutions for client engagements.
- Serve as a trusted technical advisor to client stakeholders, including technology leaders, data leaders, architects, and business partners.
- Facilitate discovery sessions, requirements workshops, technical assessments, architecture reviews, and solution-design discussions.
- Translate business goals, data challenges, and operating-model requirements into practical data strategies, roadmaps, reference architectures, and implementation plans.
- Define scalable, secure, reliable, and cost-effective architectures for data ingestion, transformation, storage, governance, orchestration, analytics, and data consumption.
- Remain hands-on in engineering work, including designing data pipelines, reviewing code, building proof of concepts, resolving complex technical issues, and establishing engineering patterns.
- Architect and implement batch, real-time, streaming, and event-driven data solutions as appropriate for client needs.
- Design modern cloud data platforms using technologies such as Snowflake, Databricks, Microsoft Fabric, Azure Data Factory, AWS Glue, Amazon Redshift, BigQuery, or equivalent platforms.
- Lead the development of robust ETL/ELT pipelines, data models, data APIs, semantic layers, and data products.
- Establish data engineering standards for code quality, testing, CI/CD, observability, metadata management, data lineage, documentation, security, and production support.
- Drive adoption of DataOps practices, infrastructure as code, automated testing, deployment pipelines, monitoring, and incident-management processes.
- Partner with data architects, data scientists, analysts, application architects, security teams, and business stakeholders to ensure solutions are aligned to enterprise architecture and business outcomes.
- Present technical recommendations, architecture options, delivery status, risks, and strategic roadmaps to both technical and executive audiences.
- Lead client-facing demonstrations, workshops, steering-committee updates, and technical presentations.
- Mentor senior, mid-level, and junior data engineers; provide technical coaching, career guidance, design feedback, and hands-on support.
- Lead and influence cross-functional delivery teams, including onshore/offshore engineers, architects, analysts, and client technical resources.
- Participate in project estimation, staffing, delivery planning, risk management, solution scoping, and proposal development.
- Support pre-sales and business-development activities, including solutioning, client presentations, technical discovery, RFP responses, estimates, and statement-of-work development.
- Stay current on emerging data, cloud, AI, governance, and analytics technologies; evaluate where they provide meaningful business value for clients.
Required Qualifications- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; equivalent professional experience may be considered.
- 10+ years of progressive experience in data engineering, data warehousing, data integration, data architecture, or related technical disciplines.
- 3+ years of experience in a technical leadership, lead engineer, solution architect, staff engineer, principal engineer, or consulting leadership capacity.
- Demonstrated experience architecting and delivering enterprise-scale data platforms and data integration solutions.
- Strong hands-on expertise in SQL and at least one modern programming language, preferably Python, Scala, Java, or C#.
- Strong experience with ETL/ELT design, data pipeline development, data transformation frameworks, orchestration, and workflow automation.
- Experience with one or more cloud providers: Microsoft Azure, AWS, or Google Cloud Platform.
- Experience with modern cloud data and analytics platforms such as Databricks, Snowflake, Microsoft Fabric, Synapse Analytics, BigQuery, Redshift, or similar technologies.
- Experience with orchestration and pipeline technologies such as Apache Airflow, Azure Data Factory, AWS Step Functions, dbt, Dagster, Prefect, or equivalent tools.
- Knowledge of distributed data-processing technologies such as Apache Spark, Kafka, Flink, Hadoop ecosystems, or comparable platforms.
- Experience designing both batch and near-real-time or streaming data solutions.
- Strong understanding of dimensional modeling, data vault, normalized data models, lakehouse architectures, data lake architectures, and data warehouse design principles.
- Experience implementing data quality, data validation, monitoring, lineage, metadata, governance, and security controls.
- Working knowledge of DevOps and DataOps practices, including Git-based source control, CI/CD, automated testing, deployment automation, and infrastructure as code.
- Strong understanding of cloud security principles, identity and access management, encryption, secrets management, and role-based access controls.
- Proven ability to lead architecture discussions and make well-reasoned technical tradeoffs involving performance, scalability, reliability, maintainability, security, and cost.
- Excellent verbal, written, and presentation skills, with the ability to explain technical concepts clearly to business stakeholders and executive audiences.
- Experience in a consulting, professional services, systems integrator, or client-facing delivery environment.
Preferred Qualifications- Experience designing data platforms that support AI, machine learning, generative AI, retrieval-augmented generation, feature stores, vector databases, or advanced analytics workloads.
- Certifications in AWS, Azure, Google Cloud, Databricks, Snowflake, Microsoft Fabric, or other relevant data technologies.
- Experience with master data management, data cataloging, data governance, privacy, regulatory compliance, or data stewardship programs.
- Experience with tools such as Collibra, Alation, Microsoft Purview, Unity Catalog, Informatica, Monte Carlo, Great Expectations, or similar governance and observability platforms.
- Experience with Salesforce, SAP, ERP, CRM, finance, supply-chain, healthcare, retail, manufacturing, or other enterprise operational data domains.
- Familiarity with BI and semantic-layer technologies such as Power BI, Tableau, Looker, ThoughtSpot, or similar platforms.
- Experience with containerization and cloud-native technologies such as Docker, Kubernetes, Terraform, CloudFormation, Bicep, or Pulumi.
- Prior experience contributing to proposals, estimates, statements of work, or technical sales pursuits.
- Experience managing or leading distributed onshore/offshore delivery teams.
