Design, Build and Maintain Scalable Data Pipelines

Design and implement resilient ETL and ELT pipelines that consolidate data from APIs, databases, event streams, and third-party platforms. We build automated, versioned workflows using Airflow, dbt, and CI/CD, add data quality checks and observability, implement retries and idempotent transforms, and ensure reliable delivery of curated datasets for analytics and ML workflows.

Architect, migrate, and optimize cloud warehouses and lakehouses on Snowflake, BigQuery, Redshift, and Databricks. We design schemas and partitioning for performance, implement cost-aware storage and compute strategies, enable semi-structured data support, and deliver governance-ready environments that empower analysts and data scientists with fast, secure access to trusted data.

Build low-latency streaming platforms using Apache Kafka, Kinesis, Flink, and Spark Streaming to enable real-time analytics, personalization, and fraud detection. We design event schemas, implement exactly-once semantics where needed, manage backpressure and retention policies, and integrate stream processing with downstream stores and feature stores for immediate insights.

Deliver scalable batch and micro-batch processing using Apache Spark and Databricks. Services include PySpark and Scala development, job optimization, partitioning and caching strategies, resource sizing, and orchestration. We tune jobs for throughput and cost efficiency and embed data profiling and lineage to support reproducible ML pipelines and reporting use cases.

Implement data governance, lineage, cataloging, and security controls to meet GDPR, HIPAA, and industry regulations. We build role-based access, encryption at rest and in transit, tokenization and PII masking, automated audits and lineage tracking, and policy enforcement to reduce risk while enabling responsible data access for analytics and ML teams.

Optimize systems for throughput and cost: query tuning, indexing, partition and clustering strategies, autoscaling compute, caching, and storage lifecycle policies. We establish monitoring, SLA/SLOs, alerting and incident response for pipelines, reduce single points of failure, and implement cost observability to keep large-scale data platforms performant and predictable.

Provide vetted data engineers, architects, and managed teams for short or long-term engagements with 7-21 day deployment, global EOR and compliant hiring, and retention-focused matching. Ideal for scaling projects, accelerating migrations, or filling skills gaps in Spark, Kafka, Snowflake, or cloud platforms while maintaining continuity and measurable delivery.