FIG. DATA

SERVICES

Data Engineering Services

Stage: Services

Build resilient, observable pipelines that move trusted data from source to decision—at SLA.

10 min readLast updated: Q3 2026

Key Takeaways

Orchestration / Workflow
Source Integration & CDC
Discover & Contract Data Contracts

§01Technology Stack

Orchestration / Workflow

Airflow Dagster dbt

Streaming / Processing

Kafka Flink Spark

Data Lakes / Storage

Delta Lake Iceberg Hudi

Infrastructure / DevOps

Terraform GitHub Actions

Data Quality / Observability

Great Expectations Soda OpenLineage

§02Engineering the Data Spine

Source Integration & CDC

Tooling rail: Debezium • Fivetran • connectors • schema registry Edge: Idempotent patterns with replay and backfill safety.

ELT/ETL Orchestration

Tooling rail: Airflow/Dagster • task groups • sensors Edge: SLA-backed DAGs with error budgets and retries.

Transformations & Data Contracts

Tooling rail: dbt • tests • exposures • contracts Edge: Contract-first design reduces downstream breakage.

Lakehouse & Storage Patterns

Tooling rail: Delta/Iceberg/Hudi • compaction • partitioning Edge: Cost/performance envelopes documented for FinOps.

Observability & Lineage

Tooling rail: OpenLineage • Great Expectations/Soda • metrics Edge: End-to-end lineage wired to BI/ML consumers.

Security, Quality & Compliance

Tooling rail: KMS/HSM • tokenization • PII masking Edge: Residency/access tiers aligned to policy-as-code.

§03Methodology

Discover & Contract Data Contracts

System/SLAs mapped; contracts drafted.

Build & Harden Reliability

Pipelines with retries and observability

Operate & Optimize SRE for Data

FinOps and error budget reviews.

§04Reliable data, predictable SLAs

CDC & orchestration at scale
Contract-driven transformations (dbt)
Lineage, DQ tests, and incident runbooks
FinOps-tuned storage and compute
Security and residency baked-in

§05Assurance Strip

Assurance Strip

SLAs: Defining service-level agreements to ensure system reliability, performance, and accountability across AI initiatives
Lineage: Tracking the origin, transformation, and flow of data for transparency, compliance, and auditing purposes
DQ: Data Quality practices to ensure accuracy, completeness, consistency, and reliability of datasets
FinOps: Implementing financial management strategies and operational controls to optimize cloud and AI costs

Boardroom Kit

Data Contract Spec: Formal agreements defining schema, quality, ownership, and access rules for datasets shared across teams
SLA Runbook: Operational guide detailing procedures to meet service-level agreements and ensure system reliability
Observability Map: Comprehensive view of monitoring, logging, and tracing for AI systems to detect and resolve issues proactively
FinOps Guardrails: Financial boundaries and policies to manage costs, optimize resources, and prevent overspending in AI operations

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