Data Engineering Technology Consultant

$12,000-$19,000/month retainer

4-6 months for modern data stack implementation, 3-4 months for warehouse migration

Data engineering consultants deliver ROI through: (1) Data warehouse cost optimization (50-80% reduction = $200K-$1M/year savings), (2) Business productivity gains (analysts spend time analyzing vs waiting for queries = $300K-$800K/year value), (3) Better decision making from trusted data (5-15% revenue impact = $500K-$5M), (4) Reduced manual data work (eliminate 2-5 FTEs = $200K-$500K/year savings). Typical ROI: 5-12x within 18 months.

Overview

Data engineering consultants specialize in building scalable data pipelines, ETL processes, data warehouses, and analytics infrastructure that turns raw data into business insights. Whether you're consolidating data from dozens of sources, building real-time streaming pipelines, implementing a modern data stack, or migrating to cloud data warehouses, our consultants bring expertise in data architecture and engineering best practices. We've built pipelines processing terabytes of data daily, designed data lakes for machine learning workloads, and implemented analytics platforms supporting 100+ business users. Our data engineers understand the full stack: ingestion, transformation, storage, orchestration, quality, and governance. We work with companies drowning in data but lacking insights, organizations migrating from legacy data warehouses, and data-driven companies needing scalable foundations.

Services Offered

Data pipeline architecture and ETL/ELT development (batch and real-time)

Data warehouse design and implementation (Snowflake, BigQuery, Redshift)

Modern data stack setup (Fivetran, dbt, Airflow, Looker)

Data lake architecture for analytics and ML (S3, Delta Lake, Databricks)

Streaming data pipelines using Kafka, Kinesis, or Pub/Sub

Data quality framework and monitoring implementation

Apache Spark and distributed data processing optimization

Data governance, cataloging, and lineage tracking

Migration from legacy systems (Oracle, Teradata) to cloud warehouses

Analytics engineering and data modeling for BI tools

Common Challenges We Solve

Data scattered across 20+ tools with no single source of truth

ETL jobs failing nightly - data analysts working with stale data

Data warehouse queries taking 30+ minutes making dashboards unusable

Business users can't trust data - different reports show different numbers

Paying $50K-$200K/month for Snowflake with costs growing 30% monthly

Data pipeline so fragile that one person maintains it - company bottleneck

Can't support real-time analytics - everything 24+ hours delayed

Legacy data warehouse on-premise costing $500K/year in infrastructure

Technologies & Tools

Snowflake & BigQueryAmazon RedshiftApache Airflowdbt (data build tool)Fivetran & AirbyteApache Spark & DatabricksApache Kafka & AWS KinesisDelta Lake & IcebergPython (pandas, PySpark)SQL (advanced analytics)Great Expectations (data quality)Looker, Tableau, Metabase

Best Practices

Use ELT (Extract-Load-Transform) pattern with modern warehouses, not ETL

Implement data quality checks at ingestion and throughout pipeline

Use managed ingestion tools (Fivetran, Airbyte) before building custom connectors

Design idempotent pipelines that can safely rerun for recovery

Partition and cluster tables for query performance (save 70-90% on warehouse costs)

Use dbt for transformation logic as code with testing and documentation

Implement incremental loads to reduce processing time and costs

Set up comprehensive data observability and lineage tracking

Typical Use Cases

Consolidating data from Salesforce, Stripe, Zendesk, GA4, and 15 other sources into warehouse

Building real-time analytics pipeline for product usage metrics

Migrating from legacy Oracle data warehouse to Snowflake

Implementing modern data stack (Fivetran + Snowflake + dbt + Looker) from scratch

Building ML feature store on data lake for personalization models

Creating customer 360 view combining data from CRM, support, product, and billing

Optimizing Snowflake costs from $80K/month to $25K/month through query optimization

Pricing Guidance

Hourly Rate

$175-$350/hour

Monthly Retainer

$12,000-$19,000/month retainer

Typical Project

$50,000-$150,000 for data warehouse migration or modern data stack implementation

Pricing depends on data volume (millions vs billions of records), number of data sources, real-time vs batch requirements, and complexity of transformations. Higher for complex migrations from legacy systems or regulated industries (healthcare, finance) requiring compliance expertise.

When to Hire Data Engineering Technology Consultant

Hire a data engineering consultant when: (1) Data spread across many tools and no unified view for decision making, (2) ETL pipelines breaking frequently causing data quality issues, (3) Data warehouse costs exploding unsustainably (30%+ monthly growth), (4) Queries taking 10+ minutes making analytics unusable, (5) Want to implement modern data stack but team lacks experience, (6) Planning migration from legacy data warehouse to cloud.

Warning Signs:

Data pipelines maintained by one person who's a single point of failure
Different reports showing conflicting numbers - no trust in data
Data warehouse costs growing 30%+ monthly with no visibility into why
Data analysts spending 80% time on data prep vs analysis
ETL jobs running for 12+ hours nightly and failing 20%+ of the time

Case Study

Client Profile

E-commerce (Multi-brand Marketplace)

Challenge

Fast-growing marketplace doing $80M GMV with data nightmare. Customer data in Shopify, order data in custom Rails app, payments in Stripe, support in Zendesk, marketing in 5 different tools. No unified reporting - executives making decisions with conflicting numbers from different teams. Data team of 3 spending 90% time on manual exports and Excel hell. Attempted Redshift implementation failed after 6 months and $200K. Business intelligence impossible - simple questions taking weeks to answer.

Solution

Data engineer consultant designed modern data stack: Fivetran for ingestion from 12 sources, Snowflake for warehouse, dbt for transformations, Looker for BI. Built 150+ data models covering customers, orders, inventory, marketing, support. Implemented incremental loads and data quality checks. Set up Airflow for orchestration. Created customer 360 view combining all touchpoints. Optimized Snowflake with partitioning and clustering. Trained data team on dbt best practices and analytics engineering.

Results

Unified data from 12 sources into single source of truth in Snowflake. Data freshness improved from 24-48 hours to 15 minutes. Query performance: 95% of queries under 5 seconds (previously 30+ minutes). Business users self-serve analytics via Looker - reduced data team tickets 75%. Data team now spending 80% time on analysis vs data wrangling. Discovered $2.3M in revenue leakage from inventory sync issues and fixed it. Snowflake costs optimized to $15K/month vs projected $60K/month through smart table design. Executive team makes decisions with confidence using unified metrics.

"We were drowning in data but had zero insights. Now we have a modern data stack that just works. Our data team went from Excel hell to actually doing analytics that drives millions in value."

Completed in 5 months for full implementation

Ready to Get Started?

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