Service Practice · Data Ops

Data, treated like production.

The work that makes analytics possible is rarely the work that gets attention. Pipelines fail quietly. Schemas drift. Quality erodes. Sources multiply. Data Ops is built for organisations that need their data to operate like an engineering asset: with monitoring, ownership, and the operational discipline that production demands.

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Technology OpsData OpsBusiness IntelligenceFinance
What This Practice Covers

Five disciplines, one operating standard.

Data Ops covers the disciplines that turn fragmented organisational data into a reliable enterprise asset: pipeline engineering, modern data platform build-out, data quality and lineage, alternative-data and web-scraping operations, and database administration. Each is run as a continuous operating capability, not a project. The Practice Lead is accountable not just for the build, but for the months and years of running it. That is the structural difference between a data warehouse that works at launch and one that still works two years later.

Pipeline Engineering & Observability

End-to-end ownership of the ingestion, transformation, and orchestration layers that move data from source to consumption.

  • Tech stack: Python and Pandas for transformation logic. Apache Airflow for orchestration. AWS-hosted Kubernetes for compute. Ingestion patterns across SFTP, S3, Snowflake, and REST APIs. Open Telemetry and Prometheus for observability.
  • Coverage: Batch, streaming, and CDC patterns. Pipeline reliability monitoring with explicit SLAs. Incident response and root-cause work as part of the operating standard.

Modern Data Platform Build-Out

Design and implementation of modern data platforms, plus the operational hygiene that keeps them trusted as the source of truth.

  • Tech stack: Snowflake, Databricks, BigQuery, and Iceberg for storage and querying. dbt for transformation. Redshift and S3 for the cloud-native warehouse and lakehouse layers.
  • Coverage: Architectural decisions on storage strategy, transformation patterns, and consumption surfaces. Migration of legacy environments onto cloud-native architectures. Master data and reference data management.

Data Quality, Lineage, and AI-Assisted Automation

The discipline that protects every downstream investment, decision, and analytic that depends on the underlying data.

  • What we build: Quality control frameworks, lineage and ownership models, dashboards for data quality coverage and compliance, and incident remediation workflows.
  • AI-assisted automation: We use LLMs and machine learning to read dataset documentation, identify critical fields, and propose threshold configurations for new data feeds. This is one of the largest concentrations of AP's applied AI work in production.

Alternative-Data & Web-Scraping Operations

For data-first investment firms and intelligence platforms, we operate end-to-end alternative-data functions, from scraper design through QC through delivery to consuming teams.

  • Tech stack: Python and Pandas for cleaning and transformation. Selenium, Scrapy, Postman, and Fiddler for scraping. XPath-based extraction. Airflow for scheduling. SQL across MySQL and PostgreSQL for consumption.
  • Coverage: Source onboarding playbooks. Time-sensitive alerting and incident response. KPI and QA methodology design. Custom datasets, reports, and enriched data products delivered to investment teams against SLAs.

DBA & Database Reliability

Production-grade database administration across PostgreSQL and MySQL in cloud environments, with the disaster recovery and capacity planning disciplines that the operating standard demands.

  • Coverage: Performance tuning, replication architecture (streaming and logical), cloud-native security (IAM, encryption, compliance), backup and recovery strategy, slow-query identification, index optimisation, capacity planning, and production on-call coverage.
  • Engagement shapes: Senior DBA leadership with junior DBA execution capacity. Disaster recovery planning and drill coordination. Database modernisation and migration.
Strategic Counsel

Architecture before infrastructure.

Data architecture decisions made in the absence of strategic context are the most expensive mistakes a data leader can make. The Consulting Practice partners with senior data and engineering leaders on the questions that determine whether a data investment compounds or stalls.

Data Architecture & Platform Strategy

End-to-end design of the data platform: sources, ingestion patterns, transformation layers, storage strategy, and the consumption surfaces that sit on top. Cloud-native by default. Vendor-neutral by principle.

Data Quality & Governance Design

Frameworks for data quality measurement, ownership, lineage, and remediation, designed for organisations where data is becoming a board-level concern.

Data Operating Model Design

The roles, structures, and accountability lines that make a data function durable, including the perennially difficult question of where data sits relative to engineering, analytics, and the business.

Web-Scraping & Alternative-Data Strategy

For investment firms, the strategic question of which alternative sources are worth onboarding, what the QC and operational requirements look like, and how the function should be structured to support investment teams over time.

Outcomes

Outcomes our clients see.

Pipeline reliability above 99.5% across critical data flows

Data platform consolidation from three or more legacy environments into a single cloud-native architecture

Time-to-insight for new data products reduced from quarters to weeks

Data quality coverage moved from selective to default across the data estate

Data function recognised internally as a delivery organisation, not a bottleneck

Case Studies

The work, in practice.

Three Data Ops engagements span the breadth of what the practice delivers.

Alternative Data Operations for a Data-First Hedge Fund

Multi-year managed engagement running the proprietary alternative-data engine behind a data-first investment process, from request through QC through delivery.

See case study

Modern Data Platform & Onboarding Operations for a Global Asset Manager

Re-engineering of the firm's data ingestion, transformation, and quality layers on a Snowflake- and Iceberg-backed architecture, including AI-assisted data quality automation.

See case study

Database, DevOps & IT Operations for a Credit Intelligence Firm

Senior DBA function operating the database backbone of a global credit intelligence platform, including disaster recovery and capacity planning.

See case study
Pairs Well With

Where this practice connects.

Business Intelligence

When the data work is the foundation for an analytics or executive reporting environment.

Technology Ops

When the data layer needs to support production AI features or when DBA and DevOps coordinate.

Consulting Practice

When a data architecture or platform decision precedes the delivery work.

Talk to a Data Ops
Practice Lead.

Speak with a Data Ops Practice Lead