Backend & Data Engineering
Backend services and data platforms built in Python and Go: dbt, Airflow, Kafka, and Flink across Databricks, Snowflake, and BigQuery.
Backend systems and data platforms usually break at the seam between them. Services emit events no pipeline can parse. Pipelines reprocess entire tables to apply a day of changes. Dashboards drift until no one trusts them. Sophotech treats both as one engineering practice. Python and Go run the service side. dbt, Airflow, Kafka, and Flink run the data side. Databricks, Snowflake, and BigQuery sit underneath.
Sophotech is a European engineering agency. Senior engineers join your team under your management and build to your standards. The work below is what they practice every day. Services are designed for operations. Pipelines are built for correctness. Platforms fit the ecosystem you already run.
API & Microservices
An API is a contract before it is an endpoint. Engineers design REST, GraphQL, and gRPC services behind API gateways, with schemas versioned and reviewed before anything ships. Backend-for-Frontend composition keeps client-specific aggregation out of core services. Rate limiting, timeouts, and retry budgets are designed in from the start. Every service ships with the operational surface it needs: health probes, structured logs, metrics, and trace propagation.
Deliverables
- Versioned API contracts: OpenAPI specifications, GraphQL schemas, protobuf definitions
- Gateway configuration with routing, authentication, and rate-limiting policies
- Backend-for-Frontend services scoped to each client surface
- Contract test suites wired into CI
- Runbooks covering failure modes, timeouts, and rollback
Tools: gRPC · GraphQL · OpenAPI · Protocol Buffers · Envoy · Kong · AWS API Gateway
Backend Development
Python and Go, written by engineers who know where each fits. Python with FastAPI or Django carries the work where the ecosystem is strongest. Go handles the cases where concurrency and a small runtime footprint matter. The seniority shows in the unglamorous parts. Connection pooling is sized against the database. Async workers run bounded queues. Idempotent handlers survive retries and duplicate deliveries. Services arrive deployment-ready, tested, containerized, instrumented, and wired for graceful shutdown.
Deliverables
- Production services in Python or Go with test suites
- Async worker fleets with bounded queues and retry policies
- Database access layers with connection pooling and migrations
- Container images and manifests ready for your deployment pipeline
- Observability hooks: metrics, structured logs, distributed traces
Tools: Python · Go · FastAPI · Django · SQLAlchemy · Celery · PostgreSQL · Redis
Data Pipeline Engineering
Full-load reprocessing is the default that quietly becomes the problem. Warehouse spend grows with table size. Freshness is capped by the slowest rebuild. Late-arriving data forces rework. Engineers build incremental pipelines. Change data capture reads from operational databases, dbt models process only what moved, and PySpark transforms handle volumes SQL cannot hold.
Airflow orchestrates the whole graph with explicit dependencies, backfill paths, and alerting on missed runs. Pipelines are versioned, tested, and reviewed like any other production code.
Deliverables
- Airflow DAGs with explicit dependencies, backfill paths, and alerting
- CDC ingestion from operational databases with schema-change handling
- dbt projects with incremental models and documented sources
- PySpark transforms for volumes beyond single-node SQL
- Pipeline test suites and validation gates in CI
Tools: Airflow · dbt · PySpark · Debezium · Kafka Connect
Data Platform & Lakehouse
There is no default stack. Engineers build on the platform your data already lives in, whether that is Databricks, Snowflake, or BigQuery. Nobody arrives pushing a migration to a favorite tool. The storage layer stays constant. Delta Lake, Apache Iceberg, and Parquet keep tables in open formats, readable by the next engine as well as the current one.
The platform itself is code. Workspaces, warehouses, and access policies are provisioned with Terraform. Sophotech engineers contribute upstream to the Terraform providers that provision this layer.
Deliverables
- Lakehouse design on Databricks, Snowflake, or BigQuery
- Delta Lake, Apache Iceberg, and Parquet table implementations
- Terraform modules for workspaces, warehouses, and access policies
- Table maintenance jobs: compaction, vacuuming, partition evolution
- Storage layout and partitioning standards your teams can follow
Tools: Databricks · Snowflake · BigQuery · Delta Lake · Apache Iceberg · Parquet · Terraform
Real-Time Streaming
Anyone can connect Kafka to a consumer. The work is in what happens under load. Partitioning and consumer-group design, idempotent processing, and replay paths get built up front. Backpressure is handled by design so nobody gets paged for it.
Engineers build event-driven ingestion on Kafka, with schema registries and dead-letter queues so malformed events become tickets instead of outages. Flink comes in only where a workload genuinely needs stateful stream processing.
Deliverables
- Kafka topic design with partitioning, retention, and compaction policies
- Stateful Flink jobs with exactly-once processing semantics where the workload requires it
- Schema registry with compatibility rules enforced
- Dead-letter handling and replay procedures for malformed events
- Consumer lag and backpressure monitoring with alerting
Tools: Kafka · Flink · Kafka Connect · Confluent Schema Registry
Data Quality & Governance
Quality is enforced where the data moves, while it is still in flight. dbt tests and Great Expectations suites run inside the pipeline as gates. A failed check stops the load and pages an owner before a bad number reaches a dashboard. Lineage is captured so every metric traces back to its sources.
Governance is engineered the same way. Personal data is handled GDPR-aware, access controls run at the column level, and retention is applied in code. The controls produce the records that SOC 2, ISO 27001, NIS2, and DORA audits ask for. Your auditors certify and the pipeline supplies the evidence.
Deliverables
- dbt test suites and Great Expectations checks gating every load
- Column-level lineage from source systems to reporting layers
- Data quality dashboards with ownership and alert routing
- GDPR controls in code: masking, access, retention
- Audit evidence mapped to SOC 2, ISO 27001, NIS2, DORA
Tools: dbt · Great Expectations · OpenLineage · DataHub
Engagements are open-ended and embedded in your team, under your management and processes. Delivery direction stays with you. Sophotech, a European company, holds the employment side, covering contracts, payroll, and compliance. You interview every engineer before the engagement starts. The engagement scales up when the roadmap demands it and back down when it does not.
Explore engagement options in Talent ServicesFrequently asked questions
How do engineers integrate with an existing team and codebase?
As team members on your team. Engineers work in your repositories, follow your review process, and ship through your CI/CD from the first commit. You interview them before they start, and they report to your leads. Existing conventions win. If your house style says Django over FastAPI, that is what gets written.
How is the technology stack selected?
Your ecosystem decides. If your data lives in Snowflake, engineers build on Snowflake. Nobody arrives with a migration agenda. Where a choice is genuinely open, the trade-offs get written down. Cost, operational load, and team familiarity all go on the page, and you make the call. Coverage spans Python and Go services, dbt, Airflow, PySpark, Kafka, Flink, and the Databricks, Snowflake, and BigQuery platforms.
How is data security handled during an engagement?
Your data stays in your environment. Engineers work through client-issued accounts with least-privilege access, under your security policies. Nothing is copied to Sophotech systems. Pipelines are built GDPR-aware, with masking, retention, and access controls applied in code. The resulting records support SOC 2, ISO 27001, NIS2, and DORA audits.
Where does backend engineering end and data engineering begin?
In practice, the line sits at the event or the table. Backend work is the services that produce and serve data. Data work is the pipelines, platforms, and quality controls that move and shape it. Most production problems sit on the boundary. Sophotech staffs that boundary as one practice, so it never lands between two specialists who meet in a ticket queue.
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