Penggudangan Data Jobs

I'm looking for a freelancer to perform descriptive data analysis on a SQL-based database. The data is stored in a database or data warehouse. Key Requirements: - Expertise in SQL and data analysis - Experience with descriptive analysis techniques - Ability to interpret and visualize data insights Ideal Skills: - Proficiency in SQL - Strong analytical skills - Experience with data visualization tools and APA 7

I need an experienced Python engineer who works confidently with AWS Glue to build and manage a small suite of data-integration jobs for a Hyderabad-based project. The core of the work is to design and automate Glue ETL pipelines that pull data from our production databases, catalog it accurately, and transform it into analytics-ready tables. Here is what I expect from the engagement: • Develop, test, and deploy Glue ETL jobs in Python. • Populate and maintain the Glue Data Catalog so new tables are discoverable and properly version-tracked. • Implement efficient transformation logic that cleans, enriches, and partitions data for downstream reporting. • Optimise job performance and cost by selecting the right worker types, job parameters, and database connectio...

The goal is to design a future-proof relational backbone for a multi-channel retail platform, built natively on PostgreSQL. I already run an operational system, and its live transactional records will have to be migrated into the new structure without downtime. Everything else—users, inventory, orders—will start fresh. Core design expectations The operational schema should be fully normalised to 3NF and optimised from day one for high concurrency. Think table partitioning (range and list), well-chosen GIN and B-Tree indexes, plus JSONB columns where semi-structured flexibility makes sense. Triggers, constraints and stored procedures must enforce business logic consistently, so the application tier can stay lightweight. Analytics layer Alongside the OLTP schema I need a se...

I need an experienced Python engineer who works confidently with AWS Glue to build and manage a small suite of data-integration jobs for a Hyderabad-based project. The core of the work is to design and automate Glue ETL pipelines that pull data from our production databases, catalog it accurately, and transform it into analytics-ready tables. Here is what I expect from the engagement: • Develop, test, and deploy Glue ETL jobs in Python. • Populate and maintain the Glue Data Catalog so new tables are discoverable and properly version-tracked. • Implement efficient transformation logic that cleans, enriches, and partitions data for downstream reporting. • Optimise job performance and cost by selecting the right worker types, job parameters, and database connectio...

Job Description We need a disciplined SQL/data engineer to produce a deterministic, re-runnable script-based build (lean MVP, not enterprise infra). Goal Create a single canonical, one-row-per-company dataset anchored on UEI, then enrich it using exact UEI matches (no name guessing). Inputs (provided) USAspending extract / source tables (L24M window) DSBS export (CSV) with UEI + owner contact fields SAM Active Entity extract (CSV) with UEI + phone + officer names Written Segment B & C rules (explicit) Deliverables 1. USAspending segmentation + canonical vendor table >Filter to last 24 months >Vendor-level aggregation → one row per UEI >Apply Segment B & C logic exactly as written >Deterministic CSV output >Basic QA checks: a. no duplicate UEIs in outp...

I need an experienced Python engineer who works confidently with AWS Glue to build and manage a small suite of data-integration jobs for a Hyderabad-based project. The core of the work is to design and automate Glue ETL pipelines that pull data from our production databases, catalog it accurately, and transform it into analytics-ready tables. Here is what I expect from the engagement: • Develop, test, and deploy Glue ETL jobs in Python. • Populate and maintain the Glue Data Catalog so new tables are discoverable and properly version-tracked. • Implement efficient transformation logic that cleans, enriches, and partitions data for downstream reporting. • Optimise job performance and cost by selecting the right worker types, job parameters, and database connectio...

I have an existing analytics initiative that now needs a dedicated Redshift-based warehouse. The core objective is to design and implement a robust schema in Amazon Redshift, then ingest data coming from three different sources—our operational SQL databases, a set of RESTful APIs, and periodic flat-file drops in CSV or JSON. Here is what I’m aiming for: • A well-structured Redshift warehouse (star or snowflake schema, whichever is most appropriate) built to scale and documented clearly. • Reliable, automated ingestion pipelines for each source type. For SQL we currently use PostgreSQL and MySQL; for APIs the payloads are mostly JSON; the flat files live in S3. • Transformations that standardise data types, handle slowly changing dimensions, and enforce dat...

Federal Data Operations Specialist (Canonical Company Lists, Upstream of CRM) Engagement Type Contract / Part-time Remote Ongoing or project-based, depending on workload Engagement: Fixed-price, per-deliverable (project-based) Role ObjectiveGraviton builds outbound and CRM systems on top of canonical company role exists to create those datasets upstream of CRM, by correctly merging multiple raw data sources into clean, deterministic, company-level tables using stable , correctness, and discipline matter more than speed. Core Responsibilities You will: Merge multiple datasets with different levels of granularity into a single canonical table Enforce a primary identifier (e.g., UEI, CAGE, or equivalent) as the identity anchor Aggregate transaction-level data to the entity...

I’m looking for a data scientist based anywhere in Latin America to help me create reliable predictive models for a finance-focused project. You’ll start with large historical datasets stored in SQL and deliver models that accurately forecast key financial indicators. I work mainly with Python, so you’ll find Pandas, NumPy, Scikit-learn and, when deep learning is justified, TensorFlow already in place. If you prefer R for certain tasks, that’s perfectly fine as long as the final workflow remains reproducible. The end-user needs to consume insights through Power BI, so once the model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding is esse...