Hire the Best Amazon Redshift Developers

Clients rate our Amazon Redshift Developers
Rating is 4.8 out of 5.
4.8/5
Based on 109 client reviews
Anh N.

Hanoi, Vietnam

$20/hr
5.0
1 jobs

Need a Python data engineer who can turn operational databases into a lakehouse and in-product analytics — not another notebook? I build Iceberg lakes, Glue/Spark pipelines, Cube.js semantic layers, Postgres CDC, and the NestJS/Vue reporting layer for SaaS teams that need production data, not a prototype. With 5+ years of data and backend engineering, I can own the path from source systems through the lake, the semantic layer, and the report your users actually click. 🚀 What I can build for you: • Iceberg data lakes on AWS Glue / Spark • Tenant-scoped ETL (one org cannot stall or leak into another) • Schema evolution, compaction, and data-quality checks • Postgres CDC into Iceberg / Parquet / Athena • Cube.js semantic layers with row-level security • In-product BI: query proxy, Excel export, Vue report UI • Terraform for Glue jobs and lake infra • Athena-ready tables for analytics 🐍 Python is at the core of my data work: • Python + Spark on AWS Glue • Iceberg writers (append, overwrite, upsert) • PostgreSQL, Parquet, and Athena • Batch jobs and scheduled pipelines • NestJS / TypeScript APIs when the product sits on the lake ⚡ I can also handle the product around the data: • Vue 3 report builders • NestJS query proxies and export jobs • AWS (Glue, S3, Athena, Lambda) • Docker and GitLab CI • Multi-tenant SaaS (org + centre scope) My goal is simple: understand the source system and the question you cannot answer today, pick the right grain and security model, and ship something that runs in production. Whether you need a Glue/Iceberg lake, CDC that does not lie about LSN, a Cube.js layer with RLS, or self-serve reports with Excel export, I can take ownership from pipeline through the UI. ✅ 5+ years data / backend engineering ✅ Long-term projects welcome ✅ Open to negotiating the hourly rate ✅ Available for a trial task if required

  • ETL Pipeline
  • Data Extraction
  • ETL
  • Machine Learning
  • Machine Learning Model
  • Artificial Intelligence
  • React
  • FastAPI
  • Docker
  • Docker Compose
  • Front-End Development
  • Microsoft Azure
  • PyTorch
Daniel Fabrico S.

Chaco Pora, Argentina

$30/hr
5.0
1 jobs

¸¸♬·¯·♪·¯·♫¸¸ 𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝘁𝗼 𝗺𝘆 𝗽𝗿𝗼𝗳𝗶𝗹𝗲! ¸¸♫·¯·♪¸♩·¯·♬¸¸ I'm a Senior Data Engineer & Cloud Data Architect. I bridge the gap between fragmented raw data and high-performance, analytics-ready infrastructure. Whether you need to build a scalable data warehouse from scratch, transition from legacy ETL to modern dbt/Databricks stack, optimize costly cloud queries, or power real-time AI/ML applications, I specialize in architecting reliable, zero-downtime data pipelines across AWS, GCP, Azure, Snowflake, and BigQuery. ⚡ 𝐂𝐨𝐫𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 1. End-to-End Modern Data Stack (MDS) & ETL/ELT Pipelines Designing and deploying automated, resilient pipelines that extract, clean, transform, and load petabyte-scale data into centralized analytics hubs. ◾ Batch & Stream Ingestion: Building automated ingestion jobs from SaaS applications, REST APIs, webhooks, and legacy DBs using Fivetran, Airbyte, Kafka, and Debezium (Change Data Capture - CDC). ◾ Analytics Engineering: Modular, version-controlled transformations with dbt (Data Build Tool), custom SQL, and PySpark-complete with automated documentation and lineage tracking. ◾ Workflow Orchestration: Designing DAGs, automated retries, and monitoring alerts using Apache Airflow, Prefect, Dagster, and AWS Step Functions. 2. Cloud Data Warehousing & Lakehouse Architecture (Snowflake, Databricks, BigQuery) Structuring high-efficiency, cost-optimized databases designed for instant analytical querying and BI dashboard performance. ◾ Warehouse Optimization: Clustering keys, partitioning, materialization, micro-partitioning, and query tuning to cut monthly cloud compute/storage costs by 30%–60%. ◾ Lakehouse & Open Table Formats: Architecting Delta Lake, Apache Iceberg, and Hudi layers on AWS S3/GCP Cloud Storage using Medallion Architecture (Bronze -> Silver -> Gold). ◾ Data Modeling: Dimensional modeling (Kimball methodology), Star/Snowflake Schemas, Data Vault 2.0, and Wide Flat Tables (OBT) optimized for Looker, Tableau, and PowerBI. 3. Real-Time Data Streaming & Event-Driven Systems Enabling millisecond-latency processing for live dashboards, fraud detection, dynamic pricing, and real-time operational metrics. ◾ Event Streaming: Setting up Apache Kafka clusters, AWS Kinesis, GCP Pub/Sub, and RabbitMQ with event serialization (Avro, Protobuf). ◾ Real-Time Analytics: Developing continuous stream-processing engines using Apache Flink, Spark Streaming, and ClickHouse/RisingWave for immediate insight delivery. 4. Data Quality, Governance, MLOps & AI Infrastructure Ensuring every byte of data entering your reporting systems is accurate, secure, compliant, and ready for advanced analytics or LLM applications. ◾ Data Quality & Observability: Automated schema validation, anomaly detection, and data testing using Great Expectations, Soda, and dbt test suites. ◾ AI/ML Infrastructure: Vector database setup (Pinecone, Weaviate, Qdrant, Milvus), RAG pipeline data ingestion, and feature store integration (Feast) for AI model training. ◾ Governance & Compliance: Role-Based Access Control (RBAC), Column/Row-level masking, PII obfuscation, and automated lineage mapping for GDPR/HIPAA compliance. ⚡ 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 & 𝐅𝐫𝐚𝐦𝐞𝐰𝗼𝗿𝐤𝐬 𝐈 𝐇𝗮𝘃𝐞 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 - 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Apache Airflow, Prefect, Dagster, Mage, AWS Step Functions, MWAA - 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 & 𝗘𝗻𝗴𝗶𝗻𝗲𝘀: Snowflake, Google BigQuery, AWS Redshift, ClickHouse, Trino/Presto, DuckDB - 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 / 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: Databricks, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue, PySpark, Apache Spark - 𝗘𝗧𝗟 / 𝗘𝗟𝗧 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: dbt (Core & Cloud), Airbyte, Fivetran, Kafka Connect, Debezium, Meltano - 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 & 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴: Apache Kafka, AWS Kinesis, GCP Pub/Sub, Apache Flink, Spark Streaming, RabbitMQ - 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 (𝗡𝗼𝗦𝗤𝗟 & 𝗥𝗗𝗕𝗠𝗦): PostgreSQL, MySQL, MongoDB, Redis, Cassandra, DynamoDB, Pinecone, Qdrant - 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 & 𝗦𝗾𝗹: Python (Pandas, Polars, PySpark, SQLAchemy), SQL (Advanced Dialects), Scala, Bash, Go - 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗗𝗲𝘃𝗢𝗽𝘀: Terraform, Docker, Kubernetes, AWS (S3, EC2, ECS, Lambda), GCP, Azure, GitHub Actions, CI/CD - 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Great Expectations, Soda, Monte Carlo, OpenLineage, Datahub ⚡ 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸: I am hired to design production-grade data pipelines, modernize legacy data stacks, fix slow analytics queries, and bring software engineering best practices (Git, CI/CD, unit testing, modular code) into data infrastructure. I prioritize clean lineage, cost efficiency, ironclad data security, and zero-downtime migrations. ⚡ 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲: ✔️ Pipelines built to scale ✔️ Massive cloud bill reduction ✔️ Production-grade reliability ✔️ Software engineering rigor ✔️ Clear communication 👉 𝗖𝗹𝗶𝗰𝗸 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 - 𝗹𝗲𝘁'𝘀 𝘁𝗮𝗹𝗸.

  • SQL
  • Python
  • ETL Pipeline
  • Data Mining
  • Data Integration
  • Data Analysis
  • ETL
  • Big Data
  • Data Engineering
  • Data Warehousing & ETL Software
  • Database Architecture
  • Database Design
  • Machine Learning
  • BigQuery
  • Apache Spark
  • Data Warehousing
  • Amazon Web Services
  • Data Scraping
  • Data Migration
  • dbt
Sophonie N.

N'Djamena, Chad

$30/hr
5.0
3 jobs

Hi, thank you for visiting my profile! I am a passionate Sr. Data Engineer with 6+ years of experience helping companies and individuals achieve their goals through effective data solutions and software development. Detail-oriented and dedicated, I ensure I fully understand your project requirements and bring my best to deliver high-quality, impactful results. Coding and problem-solving are at the heart of what I do, and I strive to write clean, maintainable code. Fluent in English and French, I’m flexible, knowledgeable, and always ready to offer valuable ideas and improvements to your project. I've worked with High Profile Clients/Organization in my career, including the following to illustrate some of them: ✅BBOXX Africa Management ✅ Techaffinity ✅ SolvIT Africa ✅ ILNET – TELECOM GOUP LTD ✅ ICT For All In All ⭐ Here's what I can bring to your project ⭐ ✅ dbt Expertise: Skilled in developing and managing dbt (Data Build Tool) transformations for data warehouses, including Redshift. I can optimize your data models, streamline workflows, and ensure your data is structured to support robust analytics. ✅ Python Proficiency: Expert in Python programming and frameworks like Django and FastAPI, enabling efficient, scalable web application development. ✅ Data Pipeline Development: Experienced in building ETL and ELT pipelines to transform, clean, and integrate large datasets across cloud environments. ✅Ability to design and develop efficient and scalable web applications ✅ Database Management: Proficient in designing and integrating databases like MySQL, PostgreSQL, MongoDB, and Redshift, ensuring data is optimized for your business needs. ✅ API Development: Strong background in creating RESTful APIs with security measures, providing secure and seamless integrations with third-party services. ✅ Version Control and Agile Methodologies: Experienced with Git and Agile frameworks, allowing for seamless collaboration in team settings and rapid iteration. ✅ High-Level Problem Solving: I am skilled at troubleshooting technical issues and providing solutions that align with your business goals. ✅Ability to integrate with third-party APIs and services ✅ Testing and Quality: Proficient in using PyTest and Unittest for unit and integration testing, committed to maintaining high code quality. ✅ Commitment to producing high-quality code and meeting project deadlines. ✅Ability to work effectively as part of a team and collaborate with front-end developers, designers, and project managers ✅ Someone who cares about helping you succeed and bringing value to your business ⭐ Why you should choose me over other freelancers ⭐ ✅ Client-Centric Approach: I prioritize delivering value and building long-term trust with all my clients. ✅ Over-Delivering Mindset: Going beyond expectations is central to my work ethic, aiming to leave clients genuinely impressed. ✅ Clear Communication: I’m highly responsive and keep communication lines open to ensure project alignment. ✅ Resilience and Problem Solving: I tackle challenges head-on, working tirelessly to find solutions. ✅ Empathy and Kindness: I treat every client and project with respect, focusing on understanding needs and adding meaningful value. I am excited to collaborate with you, providing reliable, high-level solutions to your design and development challenges. Let’s discuss how we can fully meet your business needs and take your project to the next level!

  • Amazon Redshift
  • GitLab
  • Data Warehousing & ETL Software
  • Git
  • Python
  • PostgreSQL
  • GitHub
  • Django
  • RESTful API
  • Web Development
  • dbt
  • Apache Airflow
  • Docker
  • Python Script
  • Jenkins
Shantanu J.

Indore, India

$40/hr
5.0
168 jobs

🏆 TOP RATED PLUS Data Expert | Top 3% on Upwork 💰 $600K+ earned | 16,000+ hours | 130+ clients served I am a Sr Data Engineer with expertise in developing robust AI Agents & Analytics layer. I bring over 8 years of hands-on experience with: - Building scalable ETL data pipelines that fetch raw data froms APIs and store it into data warehouses hosted over GCP/AWS (BigQuery | Snowflake | Redshift). - Developing Business Intelligence reports and dashboards using Data Studio (formerly Looker Studio), Metabase, Looker, Tableau etc. - Building powerful AI Agents using Gemini, Claude, OpenAI, Dialogflow. - Track user behaviour data using GA4 and Google Tag Manager. I’ve worked with 130+ clients across eCommerce (Shopify, WooCommerce), digital marketing & paid ads (Meta, Google Ads), mobile apps & gaming analytics, SaaS & web apps, and data-driven businesses in education, clean energy & media. 💡 What I do (End-to-End Ownership) 1. Data Engineering & Warehousing: - Build scalable, reliable data pipelines using BigQuery, Snowflake, Redshift, Python and APIs of data sources - Automated ETL (Airflow, APIs, Fivetran, custom Python scripts) - Single source of truth across marketing + product + revenue 2. Analytics & BI (Decision Systems, not just dashboards): - Executive dashboards (Looker, Data Studio (formerly Looker Studio), Metabase, Power BI, Tableau) - KPI frameworks aligned to revenue - Cohort, LTV, attribution & funnel analysis 3. Marketing & Web Tracking (Accuracy = $$$) - GA4, Google Tag Manager, Server-side tracking - Meta CAPI, Google Ads, TikTok tracking - Fix broken attribution & data loss 4. Generative AI & Automation - AI agents & workflows (OpenAI, Gemini, Claude) - Automate reporting, insights, and ops - Use AI where it actually improves ROI (not hype) 📈 Real Outcomes I’ve Delivered ✔ Built full marketing data warehouse → improved spend efficiency by 30%+ ✔ Fixed tracking & attribution → recovered lost revenue visibility ✔ Automated reporting → saved 20+ hrs/week for teams ✔ Delivered exec dashboards → faster, data-backed decisions 🧠 Why Clients Choose Me - I think like a business owner, not just an engineer - I focus on revenue impact, not vanity metrics - I handle end-to-end (tracking → pipelines → dashboards → insights) - Strong communication + fast execution (no hand-holding needed) 📈 My Tech Stack: - Business Intelligence & Data Visualisation: Google Data Studio (formerly Looker Studio) , Looker, Metabase, Power BI, Mode, Tableau, Databox, Zoho Analytics, DOMO, Google Sheets, etc. - AI: LLMs like OpenAI, ChatGPT, Gemini, Claude, DeepSeek, and GCP's services like Document AI, DialogFlow, CCAI, etc. - Engineering: SQL, Python, Airflow, APIs, Cloud Functions, Lambda Functions, Cloud Composer, Cloud Run - Data Warehouses: BigQuery, Redshift, MS SQL, MySQL, PostgreSQL, Snowflake, and Azure. - ETL & Webhook tools: n8n, Fivetran, Stitch, Windsor, Supermetrics, Power My Analytics, Saras Analytics, Zapier, Make, etc. - Tracking: Google Tag Manager, Google Analytics 4, Meta Ads Conversion API, Google Ads Conversion tracking, Stape, Server-side tracking. - Data Sources: Shopify, WooCommerce, BigCommerce, Meta Ads, Google Ads, TikTok Ads, Pinterest Ads, LinkedIn Ads, Apple Ads, Amazon Ads, Bing Ads, Google Analytics 4, Google Search Console, Google My Business, HubSpot, Active Campaign, PipeDrive, Facebook Page Insights, Instagram Insights, Stripe, SEMRush, MailChimp, Klaviyo, ClickUp, Ahref, etc. 👉 Let’s Work If you’re looking for someone who can own your entire data stack and turn it into a revenue engine, let’s talk. Click “Invite” and let’s discuss your use case 🚀 ----- 🔍 𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦 GA4, Google Analytics 4, Google Tag Manager (GTM), Server-side Tracking, Meta Conversion API (CAPI), Google Ads Conversion Tracking, Marketing Attribution, BigQuery, Snowflake, Redshift, Data Warehouse, Big Data, Data Engineering, ETL, ELT, Data Pipelines, Apache Airflow, Airflow DAGs, PySpark, Spark, Databricks, SQL, Python, Advanced SQL, Data Modeling, Data Transformation, Data Architecture, Data Lakes, Data Studio, Looker Studio, Power BI, Tableau, Data Visualization, Dashboard Development, Business Intelligence (BI), KPI Dashboard, Reporting Automation, Shopify Analytics, WooCommerce Analytics, Marketing Analytics, Product Analytics, Funnel Analysis, Cohort Analysis, LTV Analysis, Retention Analysis, Generative AI, OpenAI, ChatGPT, Gemini, Claude, AI Agents, AI Automation, AI Agents, LLM Applications, n8n, Workflow Automation, No-code Automation, Low-code Automation, Zapier, Make (Integromat), API Integrations, Webhooks, Stripe, HubSpot, Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads, Cloud Platforms (GCP, AWS), Cloud Functions, AWS Lambda, Data Orchestration

  • Amazon Redshift
  • Amazon Web Services
  • Apache Airflow
  • BigQuery
  • Business Intelligence
  • Python
  • Google Tag Manager
  • SQL
  • Data Science
  • Looker Studio
  • Google Cloud Platform
  • Artificial Intelligence
  • Data Engineering
  • ETL Pipeline
  • Data Warehousing
  • Data Visualization
  • Tableau
  • Generative AI
  • AWS Lambda
  • Big Data
Roman Z.

Kyiv, Ukraine

$55/hr
5.0
1 jobs

✅ 8+ Years as a Data Engineer 📊 Successful Data Engineering Projects Across 22+ Industries in 5 Countries 👨‍💼 Leading a Team of Data Engineers 🌎 Fluent English Hi, I’m Roman — Senior Data Engineer, Data Engineer Consultant, and Enterprise Data Engineer with extensive experience building scalable data platforms, cloud ecosystems, and modern analytics infrastructure. As a Data Engineer, I help companies design, optimize, and modernize enterprise data environments that transform raw information into actionable business intelligence. My background as a Data Engineer covers the full lifecycle of data engineering — including ETL development, cloud migration, data warehouse architecture, real-time processing, and analytics automation. Why Clients Choose Me ⚡ Senior Data Engineer experienced in enterprise-scale ETL and cloud solutions 📈 Data Engineer focused on scalable analytics architecture and performance optimization 🌎 Fluent English communication and stakeholder management 👨‍💼 Lead Data Engineer with experience managing distributed engineering teams 🔍 Business-oriented Data Engineer focused on automation and measurable outcomes Core Expertise Data Engineering & ETL: Azure Data Factory (ADF), SSIS, Apache Spark, Databricks, PySpark, Apache Kafka, dbt Core, Data Pipeline Automation, Data Migration & Transformation, Batch & Real-Time Processing — delivered by an experienced Data Engineer. Data Warehousing & Architecture: Enterprise Data Warehouse (DWH), Lakehouse Architecture, Databricks Lakehouse, OLAP & SSAS, Data Modeling, Data Governance, Azure Synapse Analytics, Snowflake, BigQuery — implemented by a Senior Data Engineer. Databases: Microsoft SQL Server, PostgreSQL, MySQL, Oracle, Azure SQL Database, MongoDB, Neo4j, Redis, SQLite — managed and optimized by a Data Engineer. Cloud Platforms: Microsoft Azure, AWS (Glue, Athena, S3, MSK, EC2, RDS), Snowflake, Office 365 — modern cloud ecosystems built by a Data Engineer. Programming & Analytics: Python, SQL, Power BI, DAX, Power Query, API Integrations, Data Automation — advanced technical expertise from a Data Engineer. 💼 Services I Offer 🛠️ ETL Pipeline Development Data Engineer building scalable ETL/ELT pipelines Data Engineer automating ingestion and transformation workflows Data Engineer optimizing existing processes and infrastructure Data Engineer integrating APIs, SaaS, ERP, and CRM systems 🛠️ Data Warehouse Engineering Data Engineer designing enterprise DWH solutions Data Engineer implementing scalable analytics platforms Data Engineer creating OLAP models and data marts Data Engineer improving governance and data quality frameworks 🛠️ Cloud Data Engineering Data Engineer implementing Azure cloud data platforms Data Engineer leading cloud migration and modernization initiatives Data Engineer designing lakehouse architecture solutions Data Engineer processing big data using Spark and Databricks 🛠️ Data Integration & Automation Data Engineer connecting SQL, APIs, Excel, ERP, and CRM systems Data Engineer automating reporting and workflows Data Engineer configuring orchestration and refresh processes Data Engineer centralizing enterprise analytics ecosystems 🛠️ BI & Analytics Support Data Engineer preparing optimized datasets for reporting Data Engineer improving Power BI data models Data Engineer supporting executive dashboards and KPI tracking Data Engineer increasing reporting scalability and performance As a dedicated Data Engineer, my goal is to help businesses create reliable, scalable, and future-ready data platforms that support analytics, automation, and strategic decision-making. Whether you need a Senior Data Engineer for ETL development, a Cloud Data Engineer for migration projects, or a Lead Data Engineer to architect a complete analytics ecosystem — I can help. 📩 Send me a message and let’s discuss how a Data Engineer can accelerate your business goals.

  • Data Engineering
  • Data Analysis
  • ETL Pipeline
  • SQL
  • Data Warehousing
  • Microsoft Azure
  • Data Migration
  • Python
  • Cloud Migration
  • Databricks Platform
  • Data Cleaning
  • Data Integration
  • PostgreSQL
  • Data Processing
  • Microsoft SQL SSAS
  • dbt
  • Data Center Migration
  • ETL
  • Machine Learning
  • Microsoft Power BI
Vebri S.

Jakarta, Indonesia

$25/hr
5.0
1 jobs

Are your data pipelines failing silently, or is your cloud data warehouse bill spiraling out of control? I help data teams and startups design, build, and optimize reliable modern data platforms across AWS and GCP - ensuring zero data loss, predictable pipeline runs, and cost-efficient query performance. Core Focus Areas & Solutions: - Pipeline Orchestration & Modeling: Production-grade Apache Airflow DAGs, dbt transformations, modular ELT architectures. - Data Lake & Warehouse Design: Modern storage and modeling on Amazon Redshift, BigQuer, and S3. - Cost & Performance Optimization: Partitioning/clustering tuning, cluster resizing, query debugging, and infrastructure refactoring to reduce monthly cloud spend. - Automated & Resilient Ingestion: CDC ingestion, API connectors, error handling, automated alerts, and schema evolution handling. Tech Stack: Cloud: AWS (S3, Redshift, ECS, Lambda, IAM), GCP (BigQuery, Cloud Storage, GCF) Data Tools: Apache Airflow, dbt, Docker, Airbyte, Kafka Languages: Python (Pandas, Polars, PySpark), Advanced SQL, Bash Whether you need to migrate legacy pipelines, fix brittle ETL workflows, or optimize your data warehouse infrastructure from the ground up, let's connect and discuss your architecture.

  • Amazon Redshift
  • Data Engineering
  • Data Lake
  • Data Ingestion
  • ETL
  • Python
  • SQL
  • Apache Airflow
  • Google Cloud Platform
  • Amazon Web Services
  • BigQuery
  • Data Warehousing
  • Data Integration
  • dbt
  • Kubernetes

How it works

Post a job for freePost a job

Tell us what you need. Create your own job post or generate one with AI then filter talent matches.

Hire top talent fast

Consult, interview, and hire quickly, so you can meet the freelancers you're excited about.

Collaborate easily

Use Upwork to chat or video call, share files, and track project progress right from the app.

Payment simplified

Manage payments in one place with flexible billing options. Only pay for approved work, hourly or by milestone.

Don't just take our word for it

What does an Amazon Redshift developer do?

An Amazon Redshift developer builds and optimizes data warehouse structures within the Amazon Redshift cloud platform. This specialist writes complex SQL code to transform raw data into actionable business insights while managing high-volume data ingestion pipelines. They configure table architectures to maximize query speed and minimize storage costs for large datasets. Their work directly supports analytics teams by maintaining reliable, fast-access databases that handle massive concurrent user requests.

  • Designs Redshift table layouts by selecting specific distribution styles and sort keys to optimize query performance. The developer analyzes access patterns to choose between key, even, or auto distribution strategies that prevent data skew. They define sort keys to accelerate range-restricted queries and improve overall system efficiency during heavy analytical loads.
  • Authors SQL stored procedures and user-defined functions to encapsulate reusable database logic. These scripts automate multi-step data transformations and enforce consistent business rules across the warehouse. The developer tests these functions rigorously to ensure they handle edge cases and maintain data integrity during complex operations.
  • Implements bulk data loading processes using COPY commands to ingest information from Amazon S3 buckets. This approach leverages parallel processing capabilities to load terabytes of data rapidly without blocking other database operations. The developer monitors load errors and validates record counts to guarantee complete and accurate data transfer into target tables.
  • Configures Redshift Spectrum external schemas to query data stored in open formats like Parquet or JSON directly from S3. This setup allows analysts to join historical archive data with current warehouse records without moving files into the main cluster. The developer manages external table definitions and ensures proper IAM roles grant secure access to the underlying storage locations.
  • Tunes database performance by analyzing query execution plans and identifying bottlenecks in long-running reports. They adjust vacuum and analyze operations to reclaim storage space and update table statistics for the query optimizer. This ongoing maintenance prevents performance degradation as data volumes grow and usage patterns shift over time.

How to hire an Amazon Redshift developer on Upwork

Step 1: Post a job

Define your data architecture needs clearly to attract specialists who build optimized Redshift schemas. The Job Post Generator powered by Uma™, Upwork's Mindful AI drafts a complete post from a few sentences about your requirements. You can write a new post, update a saved draft, or reuse an existing post to start hiring immediately.

  • Specify required distribution styles and sort key strategies so candidates demonstrate expertise in query optimization techniques.
  • List specific data sources like Amazon S3 to confirm experience with COPY commands for parallel bulk loading.
  • Request examples of stored procedures or user-defined functions to verify ability to encapsulate complex database logic.

Step 2: Evaluate candidates

Review portfolios for evidence of schema design and performance tuning in large-scale data warehouses. Uma runs instant video interviews and builds shortlists with side-by-side comparisons to highlight top matches for your project.

  • Look for documented table definitions that explain choices between KEY, EVEN, or AUTO distribution for specific workloads.
  • Check for Redshift Spectrum implementations where the freelancer registered external schemas to query data in place.
  • Verify experience with SQL transformations that improved query speed through iterative rework of database objects.

Step 3: Interview your top choices

Discuss technical approaches to data ingestion and schema evolution during live conversations. Schedule and conduct interviews within Upwork Messages to receive an immediate transcript and summary after each session.

  • Ask how they handle data type conversions and error handling during high-volume COPY operations from external sources.
  • Request explanations of how they choose sort keys to minimize disk I/O for frequent query patterns.
  • Discuss their process for debugging slow queries and applying vacuum or analyze commands to maintain performance.

Step 4: Agree on scope and begin work

Set clear milestones for delivering table structures, load scripts, and documentation before starting. Use Upwork Messages and the contract workroom for communication and project management, plus identity verification, payment protection, hourly tracking, and project funds for security.

  • Define deliverables such as SQL stored procedures and function code deployed directly into the Redshift database.
  • Establish acceptance criteria for data load scripts that verify row counts and data integrity after ingestion.
  • Require documentation of all schema design decisions and external table definitions for future maintenance reference.

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.

How much does hiring an Amazon Redshift developer cost?

$500-$1,500 per project is a typical range for focused Amazon Redshift developer work. Final pricing depends on scope, technical complexity, required integrations, source-material quality, revision needs, and the freelancer's experience level.

Schema design and optimization

$500-$1,200/project

Entry-level to mid-level
  • Redshift tables with distribution styles and sort keys
  • Strategy for query speed and data layout
  • Notes on schema decisions and object usage

Data ingestion setup

$1,200-$2,500/project

Mid-level
  • COPY commands to ingest data from S3
  • Tests to confirm accurate data loading
  • Logic to manage failed load attempts

SQL logic development

$2,500-$4,500/project

Mid-level to senior-level
  • Reusable SQL functions for database operations
  • Custom logic written in SQL or Python
  • Scenarios to verify function outputs

Redshift Spectrum integration

$4,500-$7,000/project

Senior-level
  • Definitions for accessing external data catalogs
  • Mappings to query data outside Redshift storage
  • Rules for secure cross-database queries

Full warehouse architecture

$7,000-$12,000/project

Expert-level
  • Integrated flow from ingestion to analysis
  • Optimized distribution and sort key strategies
  • Complete documentation of system design

Frequently asked questions

Is hiring an Amazon Redshift developer worth it?

For most businesses, yes: hiring an Amazon Redshift developer is worthwhile. This specialist configures distribution styles and sort keys to speed up queries on large datasets. They also write stored procedures to automate complex data transformations inside the database.

How do I evaluate Amazon Redshift developer candidates?

Review their approach to schema design by asking how they choose distribution keys for specific query patterns. A strong candidate explains why they selected a key style to minimize data movement during joins.

What is the difference between an Amazon Redshift developer and a data engineer?

An Amazon Redshift developer focuses on optimizing SQL logic and table structures within the Redshift warehouse. A general data engineer often builds the broader pipelines that move data into the warehouse from various sources.

Can an Amazon Redshift developer query data stored in Amazon S3?

Yes, they define external schemas to access data in Amazon S3 through Redshift Spectrum. This setup allows them to run SQL queries on external files without loading the data into Redshift tables first.