Your data exists. It just lives in 8 places that don't agree with each other.

Data pipelines that extract from your source systems, apply documented business logic transformations, and load into your analytical layer on a defined schedule, replacing the analyst's Monday-morning data export ritual with a reliable automated process that runs whether or not the analyst is at their desk. Source system coverage: SaaS APIs with full pagination and incremental extraction (Salesforce REST API with SOQL delta queries using LastModifiedDate filter, HubSpot Search API with hs_lastmodifieddate filter, Stripe Events API, Shopify REST Admin API with updated_at_min); relational databases via Fivetran or Airbyte managed connectors for supported sources (PostgreSQL, MySQL, MSSQL, Oracle), or custom Python connectors for unsupported sources; MongoDB change streams for document database sources; flat file and SFTP-based sources (CSV, Excel, JSON, XML). ELT architecture default: raw data landed in the warehouse staging schema in source-faithful format using Fivetran (for supported connectors) or Airbyte (self-hosted for compliance-sensitive sources); transformation layer built in dbt with staging models, intermediate models, and mart models; dbt models version-controlled in Git with CI/CD testing via dbt Cloud or GitHub Actions. Pipeline orchestration: Apache Airflow on AWS MWAA or Google Cloud Composer for complex multi-dependency DAGs; Prefect for lighter orchestration requirements with faster setup; each pipeline task has configurable retry logic (max 3 retries, exponential backoff), alert on failure (Slack webhook or PagerDuty), and SLA monitoring that pages the on-call engineer if the pipeline has not completed within the expected window.

Centralised data warehouse designed around your core business entities and the analytical queries your teams actually run, not a generic schema that requires analysts to write complex joins for every question. Dimensional data model following Kimball methodology: fact tables (fct_orders, fct_transactions, fct_events) recording business events with measures; dimension tables (dim_customers, dim_products, dim_dates) providing descriptive attributes for filtering and grouping; slowly changing dimension (SCD Type 2) handling for attributes that change over time (customer plan tier history, product price history) so analyses can be run at any point in time against the data that was current at that date. Platform selection: Snowflake for organisations that want separate compute scaling from storage, multi-cluster warehouses for concurrent user workloads, and Snowflake Marketplace for external data enrichment; BigQuery for organisations running in GCP with tight integration to Looker, Data Studio, and Google AI Platform; Redshift for AWS-native organisations with heavy Redshift Spectrum (querying S3 data) requirements; DuckDB on PostgreSQL for smaller-scale analytical workloads that don't warrant a separate warehouse. Core entity models as the agreed single source of truth: customer defined once (dim_customers) with a documented active_customer definition that sales, finance, and customer success all use; revenue defined once (fct_revenue) with refund treatment, currency conversion, and recognition timing documented and encoded in the model; all downstream reports and dashboards derived from these shared models. dbt semantic layer metric definitions expose business metrics (Total Revenue, MRR, Churn Rate) as named, version-controlled metrics that any BI tool connects to, so the metric formula lives in one place and changing it propagates to all dashboards automatically.

Event-driven data pipelines for operational use cases where the 1-hour or overnight latency of batch processing costs you money or degrades user experience, real-time fraud scoring, live inventory availability, operational dashboards that surface exceptions before customers call, and AI feature stores that serve the current feature values at inference time rather than yesterday's batch. Kafka infrastructure on AWS MSK, Confluent Cloud, or self-managed Kubernetes for high-volume event streams; topic naming convention and partition strategy designed for your event volume and consumer groups; replication factor 3 and acks=all producer configuration for durability; Confluent Schema Registry with Avro or JSON Schema for schema governance. Change data capture (CDC) from transactional databases without polling or performance impact: Debezium MySQL connector reading MySQL binlog (ROW format required); Debezium PostgreSQL connector using pgoutput logical replication; SQL Server CDC via Debezium or native SQL Server CDC/AlwaysOn; MongoDB change streams. Stream processing for transformations and aggregations: Apache Flink on AWS EMR or Kinesis Data Analytics for complex event processing including tumbling windows (5-minute revenue aggregation), sliding windows (30-minute moving average), session windows (user activity session detection), and Flink CEP pattern API for multi-event sequence detection (fraud patterns, anomaly sequences). Sub-minute data freshness: data lands in the analytical layer (Snowflake with Snowpipe auto-ingest, BigQuery Storage Write API, Elasticsearch for full-text search) within 30–60 seconds of the originating database transaction. AI feature stores (Feast or Tecton) served from Redis for sub-millisecond feature lookup at inference time, with streaming pipeline computing current feature values in real time as events arrive.

Cloud data lake architecture for organisations with high-volume event data, unstructured data (documents, images, logs), or compliance requirements mandating raw record retention that make a warehouse-only approach insufficient. Storage layer: AWS S3, Google Cloud Storage, or Azure Data Lake Storage Gen2 as the raw landing zone, with S3 Intelligent-Tiering or GCS lifecycle policies automatically moving cold data to cheaper storage tiers after 90 days. Table format: Apache Iceberg (preferred for new builds) providing ACID transactions, schema evolution (adding and renaming columns without rewriting the full dataset), time-travel queries (SELECT * FROM table AS OF TIMESTAMP '2024-01-01'), and row-level deletes for GDPR right-to-erasure compliance on the lake; Delta Lake for organisations already in the Databricks ecosystem. Medallion architecture with enforced data contracts: Bronze layer (raw, source-faithful data in Parquet format, schema-on-read, retention-for-compliance); Silver layer (cleaned, standardised, deduplicated, with schema enforced and data quality tests passing, the layer analysts and ML engineers use for exploration); Gold layer (business-logic transformed, metric-ready tables optimised for query performance, the layer BI tools and production dashboards connect to). Separation of compute from storage enables cost efficiency: AWS Athena, BigQuery Omni, or Trino for ad-hoc SQL queries on S3/GCS data; Apache Spark on AWS EMR for heavy transformation jobs; dbt with Spark adapter for dbt-managed transformations on the lake. Data governance: Apache Atlas or AWS Glue Data Catalog for metadata management, table lineage, and PII classification tagging; column-level masking of PII fields for non-privileged users.

Automated data quality monitoring integrated as a first-class deliverable in every pipeline, not added after launch when a bad number reaches the CEO. Data quality framework: dbt tests for model-level checks (not_null, unique, relationships, accepted_values) run on every dbt run with results surfaced in the dbt Cloud or dbt Core run logs; Great Expectations for table-level and column-level statistical assertions (min/max value bounds, row count within 10% of prior day, null rate below threshold, value distribution within expected range); custom SQL assertions for business-logic validation specific to your domain (every order must have a customer, every transaction must have a positive amount, refunds must not exceed original transaction amount). Check categories implemented: completeness (the orders table should have at least 80% of yesterday's row count, lower indicates a pipeline failure, not a quiet business day); freshness (data updated_at max must be within 2 hours for hourly tables, within 25 hours for daily tables); schema drift (column removed, renamed, or type changed in source system without warning, detected via Sentry Data Reliability or a custom schema comparison job that runs before each pipeline); distribution shift (Population Stability Index exceeding 0.2 for critical numeric columns indicating a unit change or data error upstream); referential integrity (customer_id in fct_orders exists in dim_customers, foreign key violations indicate a deduplication or join error). Alerting: failed checks generate a Slack alert to the #data-quality channel with the check name, the affected table, the failure detail, and a link to the last successful run; critical failures (completeness below 50%) page the on-call data engineer via PagerDuty. Data quality scorecards: weekly summary of check pass rates per table published to the data team so quality trends are visible before they become incidents.

Custom API data connectors for source systems that Fivetran and Airbyte don't cover, or that require business-specific extraction logic that off-the-shelf connectors don't implement. REST API integration with full production reliability requirements: OAuth 2.0 client credentials or API key authentication with secure credential storage in AWS Secrets Manager or GCP Secret Manager; cursor-based or timestamp-based incremental extraction (only fetching records modified since the last successful run rather than full table extraction that breaks on large datasets); automatic retry with exponential backoff on 429 Too Many Requests responses; rate limit handling that inspects the Retry-After header and queues the next request accordingly; response validation against the expected schema before writing to the data store. GraphQL API integration: introspection query to retrieve the schema; query design to extract only the fields required (no over-fetching); cursor-based pagination on connections for large result sets. Webhook receivers for real-time event capture: HTTPS endpoint deployed on your infrastructure; signature verification (HMAC-SHA256 or ECDSA depending on provider); idempotency key deduplication using Redis TTL; event stored to a raw events table immediately with async processing for transformation. Managed connector management: Fivetran or Airbyte deployed and configured for supported source systems (Salesforce, HubSpot, Stripe, Shopify, PostgreSQL, MySQL, MSSQL, BigQuery, Redshift, S3); connector health monitoring with alert on sync failure or schema change detected; connection tested and validated against the data quality framework before relying on it for production dashboards. Legacy system integration via database direct access (read-only replica connection for MSSQL, Oracle, or older PostgreSQL), SFTP file polling, or database log shipping where no API or modern connector exists.