Sr. Data Platform Engineer

Job Description

Sigil Partners is seeking an experienced Sr. Data Platform Engineer to design, build, and support scalable cloud-based data platforms for consulting clients. This individual will work closely with data, software, cloud, AI, analytics, and business stakeholders to deliver secure, reliable, and high-performance data solutions.

The ideal candidate will bring strong data-engineering and software-development fundamentals, experience building production-grade data platforms, and the ability to lead architecture, implementation, testing, deployment, and optimization activities. Experience delivering modern lakehouse, streaming, analytics, and AI-ready data platforms in client-facing environments is highly desirable.

Responsibilities

• Design, build, test, deploy, and maintain scalable cloud data platforms
• Develop reliable batch and real-time data pipelines for structured, semi-structured, and unstructured data
• Design data lakes, lakehouses, warehouses, operational data stores, and analytics platforms
• Build reusable frameworks, APIs, data services, and platform components
• Collaborate with client stakeholders to define business requirements, technical architectures, and delivery plans
• Lead technical discovery sessions, architecture reviews, design workshops, and implementation activities
• Establish standards for data modeling, integration, quality, lineage, observability, governance, privacy, and security
• Optimize data pipelines, storage, queries, and distributed-processing workloads for performance and cost
• Implement infrastructure-as-code, automated testing, CI/CD, and platform-monitoring practices
• Troubleshoot complex data, performance, reliability, and production issues
• Support data scientists, machine-learning engineers, analysts, and application teams with trusted, accessible data products
• Mentor engineers, conduct code reviews, and promote data-engineering best practices
• Evaluate emerging data and AI technologies and recommend solutions based on client requirements
• Produce architecture diagrams, technical specifications, operating procedures, and client-facing documentation

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related technical discipline
• 5+ years of professional data-engineering, platform-engineering, or software-engineering experience
• Strong programming experience with Python, Java, Scala, or a similar language
• Advanced SQL skills and experience with relational and non-relational data systems
• Experience designing and implementing production-grade data pipelines and distributed data-processing solutions
• Hands-on experience with AWS, Microsoft Azure, or Google Cloud
• Experience with modern data platforms such as Databricks, Snowflake, BigQuery, Redshift, Synapse, or Microsoft Fabric
• Familiarity with Apache Spark and workflow-orchestration technologies such as Airflow, Dagster, Prefect, or similar tools
• Experience with data modeling, data quality, metadata management, governance, and security
• Familiarity with Docker, Kubernetes, CI/CD pipelines, infrastructure-as-code, and modern DevOps practices
• Strong understanding of scalability, reliability, performance optimization, and cloud-cost management
• Strong client-facing communication, consulting, problem-solving, and collaboration skills

Preferred Qualifications

• Experience with lakehouse architectures and open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi
• Experience with streaming and event-driven technologies such as Kafka, Kinesis, Event Hubs, or Pub/Sub
• Familiarity with dbt, data contracts, data mesh, and data-product operating models
• Experience building platforms that support machine learning, generative AI, or retrieval-augmented generation
• Knowledge of vector databases, embeddings, feature stores, or MLOps platforms
• Experience implementing data cataloging, lineage, observability, and governance solutions
• Cloud, Databricks, Snowflake, or data-platform certifications
• Prior consulting or professional-services experience
• Experience leading technical workstreams or mentoring engineering teams