What are the responsibilities and job description for the Solutions Architect position at Sigmoid?
About Sigmoid Analytics:
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!
Accelerate your career with a fast-growing, innovative company. Apply now and be part of our award-winning team!
Key Responsibilities
1. Define platform vision & roadmap — architect the unified cloud-native data platform strategy covering ingestion, transformation, storage, governance, and secure access.
2. Build core platform capabilities — design and deliver lakehouse infrastructure, batch/streaming pipelines, metadata/catalog services, observability, and security on Snowflake and/or Databricks.
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.
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.
6. Deliver governed data products — build curated semantic/serving models and publish consumption interfaces via BI tools and/or APIs with proper governance.
7. Optimize cost & performance — tune pipelines and queries using partitioning/clustering, caching, archiving, lifecycle management (retention/purge), and workload optimization.
8. Own platform operations — manage on-call, incident response, backfills, root-cause analysis, post-incident reviews, and continuous reliability improvements.
9. Lead & scale engineering teams — build and lead a multi-squad organization; hire, mentor, and develop technical leaders while ensuring delivery and operational excellence. 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. 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. 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. 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.
4. 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. 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. 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. Lakehouse platform expertise — working knowledge of Databricks, Snowflake, Trino/Presto, and Apache Iceberg; hands-on experience with interoperability, connectivity patterns, and multi-engine query access.
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 Meta store, or equivalent for data discovery, lineage, and access control.
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. 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.