{"id":121409,"url":"https://alion.io/job/ergobite-sr-snowflake-data-engineer","title":"Sr. Snowflake Data Engineer","company":{"id":2821,"name":"Ergobite","domain":"ergobite.com","url":"https://alion.io/company/ergobite","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":59},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Data Vault","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Great Expectations","optional":false},{"name":"IAM","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"CI/CD","optional":true},{"name":"CloudFormation","optional":true},{"name":"Feature Store","optional":true},{"name":"ISO 27001","optional":true},{"name":"PCI DSS","optional":true},{"name":"Power BI","optional":true},{"name":"RAG","optional":true},{"name":"Tableau","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-08-25T07:40:42Z","employer_posted_date":null,"last_verified_at":"2026-08-25T07:40:42Z","board_verified":false,"closed_at":null,"days_open":52,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":52},"description":"Lead end-to-end design, build, and delivery of enterprise data platforms, lakehouse architectures, cloud data warehouses, and analytics solutions for regulated industries (banking/financial services preferred). You'll own the solution architecture, guide a team of engineers, and be the technical authority on client engagements.\nResponsibilities:\nArchitect and deliver enterprise lakehouse and data warehouse solutions on AWS (S3 Glue, EMR, Redshift) with Snowflake as the core analytical engine.\nDesign multi-zone data lake architectures (Bronze / Silver / Gold) using open table formats (Delta Lake, Apache Iceberg) with ACID compliance and schema evolution.\nBuild and optimise data pipelines batch, CDC, and real-time streaming using tools like AWS DMS, Glue Spark, MSK (Kafka), dbt, and Step Functions/Airflow.\nDefine data models: Data Vault 2.0 for integration layers, dimensional star/snowflake schemas for consumption, and semantic layers for enterprise BI.\nImplement data governance frameworks, cataloguing, lineage, data quality (Great Expectations or equivalent), MDM, and BCBS 239 / regulatory compliance controls.\nLead solution design workshops, produce architecture documents, and present to C-level and steering committees.\nMentor and technically guide a team of 4-6 data engineers across delivery phases.\nManage stakeholder expectations, delivery timelines, and quality gates on engagements sized at 6-12+ months.\nRequirements:\n7-10 years in data engineering/data architecture roles, with at least 4 years on Snowflake (Enterprise or Business Critical edition).\nStrong hands-on experience with AWS data services: S3 Glue, EMR, Redshift, DMS, Lambda, Step Functions, IAM, KMS.\nProven delivery of at least 2 large-scale data platform implementations (50+ users, 10+ source integrations, TB-scale).\nDeep expertise in SQL, Python/PySpark, and data modelling (Data Vault, star schema, bi-temporal).\nExperience with ETL/ELT orchestration tools: dbt, Airflow (MWAA), or equivalent.\nWorking knowledge of data governance, data quality frameworks, and metadata management.\nAbility to produce solution architecture documents, cost estimates, and technical proposals.\nExcellent communication comfortable leading client-facing discussions and presenting to senior leadership.\nGood to Have:\nExperience in banking, financial services, or other regulated industries (RBI/RMA compliance, PCI DSS, ISO 27001).\nExposure to AI/ML platforms SageMaker, MLOps, Feature Stores, or GenAI (Bedrock/RAG).\nSnowflake certifications (SnowPro Advanced: Architect or Data Engineer).\nAWS Solutions Architect or Data Analytics Speciality certification.\nExperience with Oracle Analytics Cloud, Power BI, or Tableau for enterprise BI delivery.\nFamiliarity with DevSecOps, CI/CD for data pipelines, and infrastructure-as-code (Terraform/CloudFormation).\nPrior experience with historical data migration and source-to-target reconciliation at 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