Data Lowdown
Services

Services.

We do hands-on data engineering and analytics work for financial services firms. Our scope spans the full data lifecycle, from source integration through to reporting and decision support. Most engagements run three to six months, though some clients keep us on retainer for ongoing pipeline maintenance and advisory. We size teams to the work — typically one to three engineers per engagement.

  1. 01

    Data Pipeline Development

    Design and implementation of batch and streaming data pipelines. We build reliable ingestion, transformation, and orchestration layers for complex financial data environments — multiple custodians, fund administrators, market data providers, and internal systems feeding into a single analytical layer. Every pipeline includes automated monitoring, alerting, and clear documentation so your team can operate it independently.

    Use cases

    Portfolio data aggregation across custodians and fund administrators. Trade reconciliation pipelines. NAV calculation feeds. Regulatory data submissions (Form PF, ADV, 13F). Market data normalization from Bloomberg, Refinitiv, and proprietary sources. Investor reporting data preparation.

    Tech
    • Python
    • SQL
    • BigQuery
    • Snowflake
    • PostgreSQL
    • Airflow
    • dbt
  2. 02

    Cloud Data Platform

    Architecture and build-out of modern cloud data platforms. We help firms migrate from legacy infrastructure — on-prem SQL Server, Access databases, FTP-based file transfers — to scalable, cost-effective cloud environments with proper governance, security, and access controls. We handle the migration planning, execution, and validation, and we build environments your team can manage after handoff.

    Use cases

    On-prem to cloud migration (GCP, Azure, AWS). Data lake and data warehouse design. Multi-environment deployment (dev/staging/prod). Cost optimization for large-scale data workloads. Infrastructure-as-code setup for reproducible environments. Disaster recovery and backup architecture.

    Tech
    • GCP
    • Azure
    • AWS
    • Terraform
    • Docker
    • CI/CD
  3. 03

    Analytics & Business Intelligence

    Reporting and analytics infrastructure that gives investment, operations, and compliance teams reliable, self-serve access to data. We build the semantic layers, dashboards, and automated reporting workflows that replace manual spreadsheet processes and reduce the volume of ad hoc data requests your team fields every week.

    Use cases

    Portfolio performance reporting for investment committees and LPs. Investor reporting packages (quarterly letters, capital account statements). Compliance dashboards for regulatory monitoring. Operational KPIs for deal sourcing, portfolio monitoring, and fund operations. Executive visibility tools that consolidate data from multiple systems into a single view.

    Tech
    • Looker
    • Power BI
    • Tableau
    • Sigma
    • Hex
    • Mode
    • ThoughtSpot
    • SQL
  4. 04

    Data Integration & API Development

    Integration with third-party data providers, CRMs, portfolio management systems, and internal tools. We build and maintain the connectors and APIs that keep your data ecosystem synchronized and reliable. Financial services firms typically operate across a dozen or more systems — we make sure they talk to each other cleanly.

    Use cases

    CRM integration (Salesforce, DealCloud, HubSpot). Market data vendor feeds (Bloomberg, Refinitiv, S&P Capital IQ). Fund admin data pulls and reconciliation. Custodian data feeds. Internal tool APIs for deal flow and pipeline tracking.

    Tech
    • REST APIs
    • FastAPI
    • Python
    • PostgreSQL
  5. 05

    Data Quality & Governance

    Monitoring, validation, and lineage tracking to ensure your data is accurate, complete, and auditable. Particularly important for firms with regulatory reporting obligations, investor-facing analytics, or multiple teams consuming the same datasets with different expectations about what the numbers mean.

    Use cases

    Data validation for regulatory filings (SEC, NFA, provincial regulators). Investor report accuracy checks before distribution. Lineage documentation for audit readiness. Data contract enforcement between data producers and consumers. Master data management for entity resolution across systems.

    Tech
    • Great Expectations
    • Data contracts
    • Lineage tooling
  6. 06

    AI & Intelligent Data Products

    Applied AI and LLM-powered solutions designed for financial services workflows. We build retrieval-augmented generation (RAG) systems, document intelligence pipelines, and classification models that transform unstructured data into structured, actionable assets. Every solution is built on top of your existing data infrastructure — not a separate AI silo — and designed to run reliably in production with proper monitoring, cost controls, and human-in-the-loop validation where it matters.

    Use cases

    RAG-powered internal knowledge bases for investment, operations, and compliance teams. Company and industry classification using LLM-powered taxonomy mapping. Automated summarization of earnings calls, legal contracts, and regulatory filings. Intelligent search across internal documents and external market data.

    Tech
    • Python
    • LangChain
    • OpenAI / Anthropic APIs
    • Pinecone
    • pgvector
    • BigQuery ML
    • Vertex AI
    • Embeddings
Engagement

How we work.

Three ways we engage, sized to the shape of the work and your team's internal capacity.

01

Embedded

Our engineers join your team on a contract basis, working inside your existing tools, workflows, and communication channels. We operate as an extension of your team — attending standups, using your repos, and shipping alongside your engineers. Typical engagements run 3-6 months, though some clients extend to 12+ months for ongoing infrastructure work.

02

Project-Based

Scoped engagements with defined deliverables and timelines — a cloud migration, a new pipeline, a reporting platform, a data governance overhaul. We start with a 2-week assessment, deliver a detailed scope and architecture plan, then build. You get working infrastructure, not a strategy deck.

03

Advisory

Architecture review, technology selection, and data strategy guidance for teams building in-house capability. We help you make the right infrastructure decisions early so you don't have to rebuild later. Advisory engagements are typically 2-4 weeks and result in a clear, actionable roadmap your team can execute independently.

Have a scoped project or ongoing data work in mind?