Key skills
SnowflakeDataOpsTerraformCI/CDPythonGitHub/GitLab/Azure DevOpsAirflow/Dagster/Step FunctionsKafka/Kinesis/SnowpipeData qualityGovernanceObservabilitySoft skillsSQLBashELTData EngineeringAnalyticsKafkaAirflowdbtAWSAzureLambdaAzure DevOpsGitGitHubGitLab
About this role
Empower is focused on transforming financial lives and fostering a flexible and inclusive work environment. The DataOps Engineer will manage the DataOps lifecycle for the Snowflake-on-AWS platform, ensuring data products are reliable and governance is maintained while automating processes and promoting best practices.
Responsibilities:
- Define and enforce data contracts (schemas, SLAs/SLOs, versioning, deprecation) for batch/streaming products; guard against breaking changes
- Maintain a schema registry/contract repo and promotion workflow (dev → test → prod) with automated checks and approvals
- Manage Snowflake objects and ELT as code (Terraform + dbt + Snowflake CLI); build Git-based pipelines with pre-merge tests (unit SQL, schema, data quality) and deterministic rollbacks
- Standardize environment topologies, seed/test data, and release calendars to reduce lead time and change failure rate
- Engineer idempotent pipelines using Streams/Tasks, Snowpipe/Kafka, and orchestration (Airflow/Dagster/Step Functions/Lambda)
- Publish runbooks and SLOs for datasets/jobs (freshness, latency, failure rate); run capacity planning and game days
- Implement the data test pyramid: column/row checks, anomaly detection, reconciliation, and end-to-end validation
- Build monitoring/alerts from ACCOUNT_USAGE/ORGANIZATION_USAGE and pipeline metadata (QUERY/LOAD/ACCESS history); wire alerts to on-call with clear ownership and auto-ticketing
- Automate PII classification and object TAGS; enforce tag-based masking, row access policies, RBAC role families, and network policies
- Ensure lineage and glossary links (Collibra/OpenLineage) are updated on every release; produce audit evidence on demand
- Lead data incident triage (bad/missing/late data), customer comms, RCAs, and post-incident hardening
- Operate change control with impact analysis, blast-radius limits, and progressive delivery (canary/backfills)
- Track queries, warehouse utilization, and job cost; implement guardrails (rightsizing, auto-suspend hygiene, query tagging/chargeback)
- Recommend workload placement (Snowflake vs. adjacent engines) balancing SLA, quality, and cost
Requirements:
- Education: Bachelor's in Computer Science, Information Systems, Data/Analytics, or related; equivalent practical experience welcomed
- Experience: 5–8+ years in data engineering/analytics platform roles with 3+ years operating Snowflake in production
- DataOps skills: You've shipped contract-first pipelines, automated tests, and environment promotion at scale; you measure success with SLIs/SLOs and error budgets
- Snowflake depth: Warehouses, Streams/Tasks, Snowpipe/Kafka Connector, search optimization, materialized views, replication/failover; strong SQL and performance tuning
- Automation: Terraform (Snowflake provider), dbt (models/tests/docs), GitHub/GitLab/Azure DevOps; Python/Bash for tooling and checks
- Observability: Building alerts/dashboards from ACCOUNT/ORG usage views; experience with data quality/observability platforms (e.g., GX/Soda/Monte Carlo/Bigeye) a plus
- Governance: Practical use of object TAGS, tag-based masking, row access policies, and evidence generation for audits