Solutions Architect

Sigmoid; New Jersey; On-site; USD 180,000 - 240,000; Full time

Job summary

Sigmoid is seeking an experienced Solutions Architect to define and lead the cloud-native data platform strategy across ingestion, transformation, storage, governance, and secure access. You will build and optimize lakehouse capabilities on Snowflake and/or Databricks, ensuring high-performance, scalable data solutions for enterprise clients.

The role requires deep expertise in Spark, Iceberg, and modern data architectures, with strong collaboration across Product, Analytics, and ML teams.

Qualifications

• 15+ years in software and/or data engineering with progression to technical leadership.
• Proficiency writing production-quality Python/SQL code and strong testing practices.
• Experience with microservices, REST/gRPC, and event-driven systems like Kafka.
• Hands-on Spark, Snowflake/Databricks, and cloud-native data pipelines.
• Knowledge of data governance, cataloging, and lakehouse architectures.

Responsibilities

• Define platform vision and roadmap for a cloud-native data platform.
• Build lakehouse architecture and data pipelines on Snowflake/Databricks.
• Optimize performance of Iceberg tables and multi-engine queries.
• Establish CI/CD, IaC, testing, and data quality standards.

Skills

Python

SQL

Java/Scala

Microservices

REST/gRPC APIs

Kafka

Java (Spring Boot)

Apache Spark

Snowflake/Databricks

Airflow/dbt/Databricks Workflows

AWS

GCP/Azure

Data modeling

ETL/ELT

Iceberg

Unity Catalog / Glue Catalog

S3/Cloud data lake

Databricks Trino/Presto

AI/ML & LLM-based analytics

Education

BS in Computer Science/Engineering

MS in Computer Science/Engineering

Tools

Git

GitHub/GitLab

Jira

Confluence

Slack

Job description

Sigmoid unlocks business value for Fortune 1000 companies through expert data engineering, data science, and AI consulting. We solve complex challenges for leaders in CPG, Retail, BFSI, and Life Sciences. With 10+ delivery centers in the USA, Canada, UK, Europe, Singapore, and India, we deliver cutting-edge data modernization and generative AI solutions. Join us to shape the future of data-driven innovation!

Why Sigmoid?

• Inc. 500 Fastest-Growing Companies (4 years running)
• Deloitte Technology Fast 500 (4 years running)
• Top Employer: AIM’s 50 Best Firms for Data Scientists
• Recently named British Data Awards Finalist & more accolades on the way!

Role :- Solutions Architect

New York/New Jersey, USA

Key Responsibilities

1. 1. Define platform vision & roadmap — architect the unified cloud-native data platform strategy covering ingestion, transformation, storage, governance, and secure access.
2. 2. Build core platform capabilities — design and deliver lake house infrastructure, batch/streaming pipelines, metadata/catalog services, observability, and security on Snowflake and/or Databricks.
3. 3. Optimize data performance — design, implement, and tune Apache Iceberg tables (partitioning, compaction, metadata) for consistent, high-performance query execution across compute engines. 4. Enforce engineering excellence — establish CI/CD, Infrastructure as Code (Terraform/CloudFormation), automated testing, schema/versioning, data quality, and end-to-end observability standards.
4. 5. Drive data governance & contracts — partner with Product, Analytics, ML, and SMEs to define data semantics, schemas, SLAs, documentation, versioning, and access controls aligned to compliance requirements.
5. 6. Deliver governed data products — build curated semantic/serving models and publish consumption interfaces via BI tools and/or APIs with proper governance.
6. 7. Optimize cost & performance — tune pipelines and queries using partitioning/clustering, caching, archiving, lifecycle management (retention/purge), and workload
7. 8. Own platform operations — manage on-call, incident response, backfills, root-cause analysis, post-incident reviews, and continuous reliability improvements.
8. 9. Lead & scale engineering teams — build and lead a multi-squad organization; hire, mentor, and develop technical leaders while ensuring delivery and operational excellence.
9. 10. Manage delivery & partnerships — oversee outsourcing partners, SOWs, capacity planning, and milestone tracking; provide hands-on technical leadership for team pods including planning, mentoring, and performance management.

Required Qualifications

1. 1. 15+ years in software and/or data engineering with a BS/MS in Computer Science, Engineering, or equivalent practical experience; demonstrated progression from hands-on development to technical leadership roles.
2. 2. Strong programming proficiency in Python and SQL (Java/Scala preferred) with a track record of writing production-quality, well-tested, maintainable code using modern testing frameworks and version control (Git, GitHub/GitLab).
3. 3. Expertise in microservices architecture, REST/gRPC APIs, and event-driven systems such as Kafka, with hands-on experience in Java (Spring Boot), multithreading, concurrency, and performance tuning.
4. 4. Hands-on Apache Spark expertise — design, troubleshoot, and optimize batch and streaming workloads with deep focus on performance tuning, cost-aware optimization, and cluster resource management. Enterprise Snowflake and/or Databricks experience — build and operate production workloads including job orchestration (Airflow, dbt, Databricks Workflows), query optimization, and day-to-day platform operations.
5. 5. Cloud-native data pipeline development — proven experience building production-grade pipelines in AWS (preferred) or GCP/Azure, leveraging services such as S3, Glue, EMR, Lambda, EC2, and IAM for secure, scalable data processing.
6. 6. Solid data architecture fundamentals — deep understanding of data modeling, ETL/ELT patterns, schema evolution, and distributed data processing principles across structured, semi-structured, and unstructured data formats (JSON, Parquet, Avro, Delta Lake).
7. 7. Lakehouse platform expertise — working knowledge of Databricks, Snowflake, Trino/Presto, and Apache Iceberg 2 hands-on experience with interoperability, connectivity patterns, and multi-engine query access.
8. 8. Metadata and catalog governance — practical experience with open table formats (Apache Iceberg) and catalog ecosystems such as Unity Catalog, AWS Glue Data Catalog, Apache Hive Metastore, or equivalent for data discovery, lineage, and access control.
9. 9. Capital Markets domain knowledge — experience in financial services with understanding of key reference and application domains such as Securities, Benchmarking, Holdings, Sales, Accounts, and Products.
10. 10. Global collaboration and communication — excellent written and verbal skills with demonstrated ability to partner effectively with engineering, operations, and business stakeholders across time zones; experience working with Jira, Confluence, and Slack for cross-functional coordination.
11. 11. AI-enabled solutioning experience — demonstrated ability to architect and deliver AI/ML and generative AI capabilities using enterprise data responsibly, including LLM-powered analytics, retrieval-augmented generation (RAG), intelligent automation, model evaluation, data privacy/security, governance, observability, and cost controls.