About Sigmoid Analytics:
Sigmoid enables business transformation using data and analytics, leveraging real-time insights to make accurate and fast business decisions by building modern data architectures using cloud and open source. Some of the world’s largest data producers engage with Sigmoid to solve complex business problems. Sigmoid brings deep expertise in data engineering, predictive analytics, artificial intelligence, and DataOps. Sigmoid has been recognized as one of the fastest-growing technology companies in North America in 2020, 2021 and 2022 by Financial Times, Inc. 5000, and Deloitte Technology Fast 500.
Having started in 2013 by three IIT Kharagpur Alumni, Rahul, Lokesh & Mayur. We have now grown into a 550+ members strong team with offices in San Francisco, Jersey City, Dallas, Peru, and Bangalore, India. Sigmoid is rapidly growing & is backed by Sequoia Capital.
Engineering Leader / Solutions Architect – Canada
Role Overview
We are looking for a senior Engineering Leader / Solutions Architect to drive the design, modernization, and scale-out of enterprise data platforms within a cloud-native lakehouse ecosystem. The role requires deep expertise across modern data engineering, platform architecture, distributed processing, and large-scale delivery leadership.
The individual will work closely with business, product, analytics, and engineering stakeholders to define scalable platform architectures, establish engineering best practices, and lead globally distributed engineering teams.
Key Responsibilities
Platform Architecture & Strategy
- Define and drive the roadmap for a unified cloud-native data platform spanning ingestion, transformation, storage, governance, observability, and secure data access.
- Architect scalable lakehouse solutions leveraging Snowflake and/or Databricks ecosystems.
- Design interoperable data platforms supporting batch, streaming, and real-time analytics workloads.
Data Engineering & Platform Development
- Build and optimize large-scale data platforms, including:
○ Lakehouse infrastructure
○ Batch and streaming pipelines
○ Metadata/catalog services
○ Data observability frameworks
○ Security and governance controls
- Design and optimize Apache Iceberg implementations including partitioning, compaction, metadata optimization, and multi-engine query performance.
Engineering Excellence & Governance
- Establish engineering best practices across:
○ CI/CD
○ Infrastructure as Code (Terraform/CloudFormation)
○ Automated testing
○ Schema management & versioning
○ Data quality controls
○ Platform observability
- Define data governance standards including schemas, SLAs, lineage, documentation, and access controls aligned with enterprise compliance requirements.
Delivery & Leadership
- Provide hands-on technical leadership across architecture, solutioning, troubleshooting, and engineering execution.
- Partner with product, analytics, ML, and business stakeholders to build governed, scalable data products.
- Manage delivery planning, partner coordination, capacity planning, milestone tracking, and operational excellence.
Platform Operations & Optimization
- Own platform reliability, incident management, root-cause analysis, and continuous operational improvements.
- Drive performance and cost optimization initiatives across cloud infrastructure, data pipelines, and compute workloads.
Required Experience & Qualifications
Experience
- 15+ years of experience across software engineering, data engineering, or platform engineering roles.
- Proven experience transitioning from hands-on engineering into large-scale technical leadership and architecture roles.
Technical Skills
Strong hands-on expertise in:
- Python / SQL, Apache Spark
- Snowflake and/or Databricks experience needed, who can develop Lakehouse platforms
- AWS cloud ecosystem (preferred)
- Apache Iceberg experience mandatory
Data Platform & Governance
○ Data modeling
○ Schema evolution
○ Metadata management
○ Catalog and lineage frameworks
○ Enterprise data governance
○ Unity Catalog
○ AWS Glue Catalog
○ Hive Metastore or equivalent metadata ecosystems
Domain Experience
- Prior experience within Financial Services / Capital Markets environments preferred.
- Familiarity with domains such as: Securities, Holdings, Benchmarking, Accounts, Products, Wealth & Asset Management platforms
Leadership & Collaboration
- Strong stakeholder management and communication skills with experience collaborating across engineering, operations, and business teams globally.
- Experience managing offshore-heavy engineering delivery models and cross-functional technical programs.
- Comfortable operating as both a strategic technical leader and hands-on problem solver where required.
