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Heads of Data Engineering are accountable for pipelines disrupted by source schema changes, rising warehouse compute costs, and recurring discrepancies between source and reporting datasets. These issues increase operational overhead and put modernization timelines at risk.
Your team can maintain agreed data freshness and quality thresholds, validate source-to-target reconciliation, and track warehouse consumption against established baselines, with defined ownership of production workloads.
Request a Data Platform AssessmentBackOfficePro addresses these challenges through dependency mapping, automated pipeline development, ETL/ELT modernization, workload optimization, and data quality controls. Our engineers validate transformations, reconcile source-to-target data, and manage deployments against agreed acceptance criteria.
Source systems, pipelines, and workload patterns are assessed to produce a current-state map, target architecture, and prioritized roadmap, with dependency analysis and platform cost estimates to support architecture and migration decisions. Our data engineering consulting work ends at design and planning. Build and migration are scoped separately.
Batch and change data capture (CDC) pipelines ingest data from databases, APIs, and files using incremental processing, retry controls, and automated failure alerts. Pipeline-level reconciliation, schema-change handling, and recovery procedures support agreed data refresh windows and reduce manual intervention when ingestion jobs fail. Excludes source application changes and dashboard development.
Warehouse and lakehouse architectures are designed around workload concurrency, data formats, query patterns, and storage requirements. Dimensional or layered data models, partitioning strategies, and access controls support consistent analytical datasets and predictable query performance across business workloads. Excludes dashboard design and report building.
Legacy ETL jobs and repositories are inventoried, dependencies mapped, and existing transformation logic converted into tested dbt or Spark workflows, with Airflow orchestration where required. Migration proceeds in planned waves to the agreed cloud data platform, with transformation parity testing, source-to-target reconciliation, and documented cutover and fallback procedures to validate migration results before production transition. Excludes application re-platforming and network infrastructure.
Kafka, Kinesis, or Apache Flink pipelines ingest and process event streams against defined latency and throughput requirements. Checkpointing, replay mechanisms, and dead-letter queues support recovery from processing failures and help manage delayed or malformed events without interrupting downstream data consumption. Excludes workloads better served by batch processing.
Workloads are profiled to find slow queries, oversized compute, and idle storage. Query tuning, compute right-sizing, and storage optimization are measured against agreed cost and latency baselines to validate performance improvements and identify regressions. Excludes software licensing negotiation and hardware procurement.
Git-based version control, CI/CD pipelines, infrastructure as code, and release approvals with rollback procedures help you standardize deployments, validate changes before production release, and maintain controlled recovery procedures for failed deployments. Excludes release management for applications outside the data platform.
Data classification, lineage, and cataloging with Collibra or Apache Atlas establish metadata visibility and traceability across datasets. Platform-native role-based access controls and masking restrict sensitive-data access, while completeness, freshness, and volume checks identify data quality issues before downstream consumption. Controls are scoped to the requirements of your engagement. Excludes legal advice and certification audits.
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BackOfficePro's data engineering engagements are governed by defined delivery responsibilities, measurable acceptance criteria, and documented production handover, giving your team a clear basis for evaluating implementation risk and operational readiness.
Architecture, build, testing, and handover sit under a single delivery owner, so decisions and defects have a clear point of accountability.
Migrated workloads run beside the legacy system and are reconciled against agreed criteria before production switches over, so your team approves cutover on evidence.
Pipeline freshness, failure rates, query latency, and workload cost are measured before changes, so improvements are reported against your environment, not generic benchmarks.
Production pipelines, transformation logic, configuration files, test evidence, monitoring rules, and recovery procedures are transferred against agreed acceptance criteria to support internal maintenance and troubleshooting.
How is a data engineering project scoped before work begins?What does onboarding look like for an existing data environment?Do you work within our existing stack or recommend replacements?What timeline should we expect for a migration or pipeline build?Do we need data engineering consulting before an implementation engagement?How do you validate migration and data quality results before handover?How do you handle security and compliance requirements in multi-cloud environments?What visibility do we get into progress during delivery?Can data engineering outsourcing work alongside our internal data team?What does a data engineering services company hand over at project close?Would you like help choosing the right plan for your business? Contact our agent, who will guide you through our customized plan, especially for you.
