Hire the Best Data Warehousing Specialists

Your GA4 numbers don't match your ad platforms. Conversion events fire twice, or not at all. And every month someone rebuilds the same dashboard by hand because last month's stopped being believable. The tracking underneath is broken, so nothing built on top of it can be trusted, and you're left guessing which number to spend against. I fix the whole chain, end to end: from the tag firing on your site, to the BigQuery pipeline that cleans and unifies the data, to the dashboard your team makes decisions on. I'm an Analytics Engineer who builds the data engineering layer (automated pipelines, BigQuery modeling, BI) on top of deep, hands-on tracking implementation roots. That combination is the point. Because I own both the server-side tracking and the pipelines it feeds, the data stays coherent the whole way down, with no hand-offs between three specialists who each blame the other when the numbers don't add up. I work almost exclusively with e-commerce brands and marketing, growth, and advertising agencies (white-label welcome). I speak ROAS, MER, CAC, and LTV, so you tell me the business goal, not the technical spec. --- What I build Marketing & e-commerce data pipelines (GCP / BigQuery), the core of what I do: - Automated ETL/ELT from GA4, Shopify, ad platforms (Meta, Google, TikTok), and CRM into BigQuery - Ingestion via Python, dbt, Fivetran, Stitch, or the GA4 native BigQuery export - BigQuery data modeling: staging, intermediate, and mart layers, partitioning, clustering, incremental models - Data cleaning, normalization, deduplication, and identity stitching across sources - Orchestration and scheduling on GCP: Cloud Run, Cloud Functions, Pub/Sub, Cloud Scheduler - Cost optimization, query tuning and table design so the warehouse scales without surprising your bill - Deliverables: automated pipeline, single source of truth in BigQuery, attribution-ready datasets, documented and modeled tables refreshed on schedule, a warehouse your next analyst can actually read Tracking & analytics implementation, the foundation the data depends on: - GA4: advanced e-commerce, custom events, funnels, attribution-ready data layer - Google Tag Manager, client-side and server-side (Stape or GCP) - Conversion APIs: Meta CAPI, TikTok Events API, Google Ads enhanced conversions, Snapchat, LinkedIn - Client-to-server migration to recover conversions lost to iOS, ITP, and ad blockers - Consent Mode v2 and CMP setup (OneTrust, Usercentrics), first-party capture, hashing, deduplication - dataLayer, JavaScript, and DOM work for custom event tracking - Deliverables: documented tracking plan, validated event and conversion setup, tag and trigger audit, numbers that reconcile across platforms BI & reporting, the part your team actually looks at: - Looker Studio dashboards powered by BigQuery (Power BI / Tableau / Metabase on request) - Revenue analytics, channel performance, funnels, ROAS/CAC/LTV scorecards - Automated SQL transformations and scheduled workflows, no manual monthly rebuilds - Deliverables: CEO-ready dashboard, automated refresh, documentation your team can follow --- Why clients hire me: I combine data engineering, analytics implementation, and BI automation into one workflow. Instead of isolated dashboards or pipelines built on shaky tracking, you get a full-stack, maintainable, accurate data system, with one person accountable from the tag on your site to the number on the dashboard. Agencies can put me behind their own client work: on calls if you want me there, invisible if you don't. Certifications: - Google Cloud Professional Data Engineer (2025) - Google Analytics Individual Qualification (verified by Upwork) - Meta Certified Marketing Developer (Credly) Whether you need a GA4-to-BigQuery pipeline, broken tracking fixed, Shopify analytics modeling, or a complete analytics foundation, tell me what you're trying to decide with the data, and I'll build the system that gets you there.

I'm a Senior Data Engineer with 8+ years of strong technical expertise in building reliable and scalable data infrastructure, from data ingestion to transformation to warehousing, streaming, and data analytics, specializing in dbt, Snowflake, Airflow, Databricks (and more) across AWS, Azure, and GCP, with robust ELT and ETL pipelines. If your data pipelines are brittle, your data warehouse is slow, or your data was never built to scale, that is exactly what I fix, with fault tolerance, observability, and audit-ready quality engineered in from day one. I cover the full data engineering lifecycle: batch and real-time data pipelines, Modern Data Stack builds, lakehouse architecture, cloud and warehouse data migration, governance, and the data foundations that feed modern systems. 🎯 Core Expertise: ✅ Data Pipelines & Orchestration: End-to-end batch and real-time pipelines with Apache Airflow, Dagster, Prefect, AWS Step Functions, and Azure Data Factory. Idempotent, schema-drift tolerant, and monitored so failures surface before they reach your stakeholders. ✅ Cloud Warehousing & Lakehouse: Snowflake, BigQuery, Amazon Redshift, Databricks, and Microsoft Fabric, with Delta Lake and Apache Iceberg lakehouse foundations governed through the Glue Data Catalog and Lake Formation, with Athena and Redshift Spectrum for serverless queries, Medallion Architecture, partitioning, and performance tuning. ✅ Data Transformation & Modeling: dbt (Core and Cloud), SQLMesh, Spark and PySpark on EMR and AWS Glue, Star Schema and dimensional modeling, analytics engineering best practices, full test coverage, and CI/CD for data models. ✅ Streaming & Real-Time Analytics: Distributed streaming with Apache Kafka, Flink, Spark Structured Streaming, Kinesis, and Pub/Sub, including exactly-once semantics, dead-letter queues, CDC, and end-to-end latency guarantees. ✅ Data Ingestion & Integration: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, and custom CDC pipelines for near-real-time sync across structured, semi-structured, and unstructured sources. ✅ Data Quality, Governance & Observability: Automated data quality frameworks, SLA monitoring, auditable lineage, data catalog and metadata management, and observability that catches bad data early. ✅ Cloud Migration & Modernization: Zero-downtime migration handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime, replacing brittle ETL and ELT with a clean Modern Data Stack. ✅ AI-Ready Data Infrastructure: Pipelines engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ------------------------------------------------------ ⚙️Tech Stack: ⚡ Warehouses & Lakehouse: Snowflake | BigQuery | Redshift | Databricks | Microsoft Fabric | Athena | Delta Lake | Iceberg ⚡ Transformation: dbt | SQLMesh | Spark | PySpark | AWS Glue | EMR | Star Schema | Medallion Architecture ⚡ Orchestration: Airflow (GCP Cloud Composer and AWS MWAA) | Dagster | Prefect | Azure Data Factory | Step Functions ⚡ Streaming: Kafka | Flink | Kinesis | Pub/Sub | Spark Structured Streaming | ClickHouse ⚡ Ingestion: Fivetran | Airbyte | Matillion | Stitch | Hevo | Meltano | CDC ⚡ Governance & Catalog: Glue Data Catalog | Lake Formation | Unity Catalog | Microsoft Purview | Dataplex ⚡ Cloud: AWS | GCP | Azure ⚡ Languages: Python | SQL (Snowflake, BigQuery, T-SQL, PL/pgSQL) | FastAPI ⚡ Databases: PostgreSQL | MySQL | SQL Server | DynamoDB | MongoDB ⚡ BI & Reporting: Looker | Tableau | Power BI | GA4 | Metabase | Superset | Streamlit | Grafana ------------------------------------------------------ ⭐ What Clients Say: 🏅 "Adarsh rebuilt our analytics pipeline on Snowflake, Airflow, and dbt, giving us reliable, version-ready data. Reporting accuracy improved overnight, and we can finally trust the numbers." – Anita, Head of Product, FinTech SaaS 🏅 "He designed a zero-downtime migration to a modern data warehouse that cut query latency by more than half while keeping our SLAs intact." – Daniel, VP of Data, AdTech Firm 🏅 "Clean architecture, solid dbt models, and Airflow pipelines running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Mark, Director of Data Engineering, E-commerce Startup 🏅 "We came to him with a Spark pipeline costing us a fortune and delivering stale data. He restructured the workflow logic and cut processing time by 70%." – Leo, Head of Analytics, HealthTech SaaS ------------------------------------------------------ 🏆 TOP RATED PLUS | EXPERT-VETTED | Top 1% on Upwork | 8+ Years Experience | 100% Job Success 🚀 Ready to build a scalable, production-ready data infrastructure to turn your raw data into reliable, actionable business insights? Click the 'Invite to Job' button on the top right, and let's discuss your data pipeline!

Most data consultancies fall into one of two camps: strategy firms that hand you a slide deck and walk away, or implementation shops that sit around waiting for you to tell them exactly what to build. Neither works if you're a mid-market company with zero to two data hires. At Data-Sleek, we own the full path from data strategy through AI enablement so you don't have to hire a team to do it well. You shouldn't have to hire a Head of Data just to start. My name is Franck Leveneur. 𝗜 𝗯𝗿𝗶𝗻𝗴 𝟯𝟬+ 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Since 2018, I've taught Data Management at the UCLA Anderson School of Management for the Master of Science in Business Analytics (MSBA) program. I founded Data-Sleek in 2020 as a boutique consulting firm built around this exact gap in the market. On Upwork, we've earned over $𝟓𝟎𝟎,𝟎𝟎𝟎 𝐚𝐜𝐫𝐨𝐬𝐬 𝟏𝟑𝟗 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝 𝐣𝐨𝐛𝐬. Clients consistently tag my work as "Committed to Quality," "Solution Oriented," and "Clear Communicator." When you work with me, you're not getting a junior freelancer learning on your dime; you're getting a 𝐛𝐚𝐭𝐭𝐥𝐞-𝐭𝐞𝐬𝐭𝐞𝐝 𝐯𝐞𝐭𝐞𝐫𝐚𝐧 who has built data ecosystems for high-growth companies like Numerade, Digital Asset Research, and Johns Hopkins, among 15+ others. ──────── 🚨𝐓𝐇𝐄 𝐏𝐑𝐎𝐁𝐋𝐄𝐌 𝐖𝐄 𝐒𝐎𝐋𝐕𝐄 Your company has data - a lot of it. But: 1. Your systems don't talk to each other (ERP, CRM, EHR, TMS - all siloed) or you don’t know where to start Comment end 2. KPI reporting takes too long, and nobody quite trusts the numbers 3. You want to use AI and predictive analytics, but your data isn't ready 4. You've tried BI tools. You still don't have real answers. This is not a tool problem. It's a foundation problem. And that's exactly what we fix. 🚀 𝐇𝐎𝐖 𝐖𝐄 𝐖𝐎𝐑𝐊 - 𝐓𝐇𝐄 𝐅𝐎𝐔𝐑-𝐏𝐈𝐋𝐋𝐀𝐑 𝐅𝐑𝐀𝐌𝐄𝐖𝐎𝐑𝐊 We guide clients through a structured data journey. You can engage at any stage: 1. Data Strategy (Discovery & Alignment, optional but recommended) Data inventories, AI readiness assessments, architecture design, and strategic roadmaps. 2. Data Centralization (Integration & Governance) Single source of truth via Snowflake, BigQuery, or Redshift connected through Fivetran and governed pipelines. 3. Data Transformation (Modeling & Preparation) dbt consulting, semantic layers, and ETL/ELT pipelines that model your data for analysts and AI. 4. Data Activation (Insights, Prediction & AI) BI, Tableau dashboards, predictive analytics, and ML wherein raw data turned into real business outcomes. DATA WAREHOUSE CONSULTING SERVICES Struggling with complex data warehousing projects can hinder your company’s ability to scale. Our data warehouse consulting services are tailored to address your business’s data needs. 🙅🏼‍♂️ 𝐃𝐀𝐓𝐀𝐁𝐀𝐒𝐄 𝐄𝐗𝐏𝐄𝐑𝐓𝐈𝐒𝐄 - 𝐓𝐇𝐄 𝐓𝐇𝐈𝐍𝐆 𝐌𝐎𝐒𝐓 𝐂𝐎𝐍𝐒𝐔𝐋𝐓𝐀𝐍𝐂𝐈𝐄𝐒 𝐂𝐀𝐍'𝐓 𝐃𝐎 Unlike modern data stack consultancies that only know cloud tools, we know how the underlying transactional systems actually work. I have 15+ years as a Senior MySQL DBA and deep expertise across: ➝ MySQL & AWS Aurora ➝ PostgreSQL ➝ ClickHouse ➝ MS SQL Server ➝ SingleStore Performance tuning, partitioning, replication, and scaling, not just dashboards on top of messy data. 🏭 𝐈𝐍𝐃𝐔𝐒𝐓𝐑𝐈𝐄𝐒 𝐖𝐄 𝐒𝐏𝐄𝐂𝐈𝐀𝐋𝐈𝐙𝐄 𝐈𝐍 ➔ Construction ➔ Healthcare ➔ Higher Education ➔ Insurance ➔ Transportation ➔ SaaS ➔ EdTech ➔ FinTech We have dedicated playbooks, industry-specific data models, and named case studies in each. If your company is in one of these five, we've already solved your problem before. 💯 𝐏𝐑𝐎𝐕𝐄𝐍 𝐑𝐄𝐒𝐔𝐋𝐓𝐒 🏆 Johns Hopkins (Higher Education): Data warehouse architecture consulting reduced reporting time by 40% and increased data accuracy by 35% within the first year. 🏆 Numerade (EdTech): Redesigned database schema and ETL processes - query times dropped from several minutes to sub-second, system outages eliminated, scaled to unlimited concurrent users, subscriber growth followed. 🏆 Digital Asset Research (FinTech/Crypto): Built a real-time ingestion pipeline handling 40 million rows daily (250M+ trades). Client expanded their client base by 600% and captured the largest market share in their category. 💡 𝐀𝐈 𝐑𝐄𝐀𝐃𝐈𝐍𝐄𝐒𝐒 𝐀𝐒𝐒𝐄𝐒𝐒𝐌𝐄𝐍𝐓 - 𝐓𝐇𝐄 𝐑𝐈𝐆𝐇𝐓 𝐖𝐀𝐘 𝐓𝐎 𝐒𝐓𝐀𝐑𝐓 𝐀𝐈 Most AI projects fail because the data foundation underneath them isn't ready; not because AI is hard. If you want predictive analytics, ML models, or intelligent automation, start here: → Fixed-fee engagement · 4-6 weeks · Clear deliverable: which AI use cases your data can support today, and the roadmap to support more. ──────── 🌱 𝐋𝐄𝐓'𝐒 𝐆𝐄𝐓 𝐒𝐓𝐀𝐑𝐓𝐄𝐃 We always start with a free initial consultation to scope the work properly before any engagement begins. Send a message - let's see if we're a fit!

Messy data slowing your team down? I build scalable ETL/ELT pipelines and modern cloud architectures on Azure, Databricks, Fabric, and Snowflake that turn raw, chaotic data into clean, analytics-ready systems fast and reliably. I bridge the gap between fragmented data sources and production-grade dashboards, seamlessly adapting to your existing infrastructure rather than forcing an expensive rebuild. What I Can Help You With: Data Warehouse & Lakehouse Architecture: Implementing Medallion design patterns (Bronze → Silver → Gold) using Delta Lake, Microsoft Fabric OneLake, and Snowflake. Scalable ETL/ELT Ingestion: Building automated, metadata-driven pipelines via Azure Data Factory, Fabric Pipelines, Databricks (PySpark/SQL), and dbt. Real-Time Data Streaming: Architecting low-latency workflows using Apache Kafka, Azure Event Hubs, and streaming engines. Database Design & Optimization: Performance tuning, indexing, and data modeling for PostgreSQL, Azure SQL, and cloud warehouses. Proven Project Highlights: Microsoft Fabric Incremental Pipeline: Built a control-table pattern using Get Metadata, Lookup, and ForEach loops to orchestrate zero-duplicate, quarterly ingestion from SharePoint into OneLake via Dataflow Gen2. Azure/Databricks Streaming: Developed a restaurant analytics platform processing 80,000+ events/day, cutting reporting lag from 6 hours to under 3 minutes. Kafka/Snowflake Pipeline: Engineered a real-time stock market data pipeline tracking 120+ tickers with under 8 seconds end-to-end latency. I write clean, documented code your team can maintain long-term and provide transparent daily updates. Message me with your data challenge and I’ll walk you through exactly how to solve it.

I'm a results-driven Senior Data Engineer specializing in building cloud-native data pipelines and architectures that transform raw data into actionable business insights. With 100% job satisfaction and a 5-star rating, I deliver solutions that exceed expectations. What I Bring: Cloud Expertise: Azure, GCP, and AWS with deep experience in Databricks, BigQuery, and Data Factory Real-Time Processing: Built streaming pipelines reducing reporting latency from hours to minutes Enterprise Scale: Consolidated 50+ data sources, processed 100M+ daily transactions, and supported 1000+ users Architecture Design: Expert in Lakehouse, Medallion, and Star Schema implementations with strong data governance Proven Results: Reduced reporting latency by 98% through real-time pipeline optimization Improved query performance by 40% with strategic data modeling Achieved 35% increase in compliance reporting accuracy Delivered zero-downtime deployments with automated CI/CD I partner closely with stakeholders to understand business needs and deliver data solutions that drive decision-making. Whether it's building real-time analytics platforms, implementing data governance, or optimizing existing pipelines, I focus on scalable, maintainable solutions. Let's discuss how I can help transform your data into a strategic asset.

I build the data infrastructure behind confident business decisions. Clean pipelines, structured warehouses, automated workflows, and dashboards that actually mean something. I am a Data Engineer and BI Developer specializing in end-to-end data solutions across the Microsoft ecosystem. I design and deliver scalable data pipelines, cloud-based warehouses, and analytics-ready environments using Microsoft Fabric, Azure Data Factory, Azure Synapse, Databricks, SSIS, Python, and SQL, paired with executive-grade Power BI dashboards that turn raw data into real business visibility. My work sits at the intersection of data engineering and business intelligence. I don't just move data. I architect structured, maintainable environments that organizations can own, scale, and trust for long-term decision-making. What I Deliver - End-to-end data pipeline design and implementation using Azure Data Factory, Microsoft Fabric, and Databricks - Data warehouse architecture with clean, well-modeled schemas (star schema, dimensional modeling) built for performance and clarity - ETL/ELT development and automation including ingestion, transformation, scheduling, monitoring, and error handling across multiple source systems - Integration of diverse data sources including CRMs, ERPs, accounting platforms (QuickBooks, Xero), ecommerce systems (Shopify, Amazon), marketing APIs, CSV/Excel files, and cloud databases - Microsoft Fabric implementation including lakehouses, warehouses, pipelines, and paginated reports within the Fabric ecosystem - SQL development, query optimization, and stored procedure design for high-performance reporting - Python scripting for data automation, transformation, and API-based data extraction SSIS package development and maintenance for legacy and hybrid environments - Interactive Power BI dashboards and reports including KPI tracking, contribution margin analysis, profitability reporting, inventory analytics, and operational visibility - Documentation, SOPs, and structured handover materials so internal teams can confidently maintain and extend solutions independently Tools & Technologies - Microsoft Fabric - Azure Data Factory - Azure Synapse Analytics - Azure Data Lake Storage - Databricks - Python - SQL Server & SQL - SSIS - Power BI (Desktop, Service, Embedded, Paginated Reports) - Power Query & DAX - Microsoft Excel How I Work - Full ownership of delivery, from understanding your data landscape through to pipeline implementation, dashboard development, documentation, and team training - Focus on building solutions that are simple, well-structured, and designed for in-house sustainability, not black boxes that create dependency - Whether you need a greenfield data platform, an existing infrastructure restructured, or a reliable reporting layer connected to your operational systems, I bring the engineering discipline and BI expertise to deliver it cleanly and on time If you need a data engineer who can own the full journey from raw data to reliable reporting, let's connect. I'd love to understand your data challenges and build something that actually works.