infrastructure setup
data storage
- provision and configure snowflake
- set up and manage data lakes (e.g. s3) storing unstructured and semi-structured data
computing resources
- provision and manage github actions runners for executing data pipelines and transformations
- set up and manage streaming platforms (e.g., apache kafka, amazon kinesis) for real-time data ingestion and processing
data integration and orchestration
data ingestion
- implement data ingestion pipelines using dbt and github actions to ingest data from various sources (e.g., databases, apis, flat files) into the data lake and snowflake
- leverage dbt models and transformations for data transformation and enrichment
workflow management
- set up and configure github actions workflows for pipelines
data quality and monitoring
data quality
- implement data profiling and data quality checks
- define and monitor data quality metrics (e.g., completeness, accuracy, consistency) using
- set up data quality dashboards and reports
system monitoring
- monitor snowflake resource utilization (e.g., credits, storage)
- implement platform performance monitoring for data pipelines
- set up log aggregation and analysis tools (e.g., elk stack, cloudwatch)
alerting and incident management
incident management
- set up alerting mechanisms for critical issues (e.g., data pipeline failures, data quality issues)
- implement alert routing and escalation processes
- configure slack as the alerting firehose
reporting and data visualization
documentation
- maintain documentation for data models, pipelines, infrastructure, and processes using github wikis or readme files
- implement knowledge-sharing practices (e.g., github discussions, internal wiki)
continuous improvement and devops
automation
- automate deployment, testing, and documentation using github actions
- implement infrastructure as code practices for managing cloud resources
training and skill development
- provide training and knowledge-sharing opportunities for team members
- encourage participation in relevant conferences, meetups, and online communities
- foster a culture of continuous learning and improvement
agent-powered data operations roadmap
phase 1: baseline worker system
- define role-based workers for data engineering, analytics engineering, and analysts
- add dbt pr impact checks (state:modified+, tests, smart diff report)
- publish worker registry, output contracts, and quality gates
- execute pilots internally at l1/l2 autonomy before any client rollout
phase 2: supervised self-learning
- log every worker run as a trace with inputs, decisions, outputs, and outcomes
- introduce evaluator scorecards for quality, safety, cost, and reliability
- propose rule/prd updates from repeated patterns with human approval gates
- canary learned behavior only for low-risk workflows
phase 3: controlled autonomy
- add canary rollout for learned behavior changes
- require rollback plans for high-impact worker updates
- track trust metrics (acceptance rate, rollback rate, reviewer override rate)
- launch client design-partner rollout after internal thresholds are met
phase 4: client productization
- package validated workflows into repeatable service offers
- define client autonomy tiers and governance defaults
- operationalize onboarding, reporting, and support playbooks